{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 主题二：数据运算与流程控制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 应用目标\n",
    "    - **阶段实战：利用各种计算、条件控制、循环控制等知识，实现各种图形绘制应用程序；**\n",
    "    - **综合实战：**\n",
    "        - **完成股票信息的完整数据查询。**\n",
    "        - **完成完整的信息输入模块。**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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5oHNA54DOAZ0DOgd0Dugc0Dmgc0DngM4BnQMXKQdYdsR2k9wDAgJ0m0gXaSfQq61zQOeAzgGdAzoHdA7oHNA5oHNA54DOAZ0DOgd0DugcaAwH+JibuLlNFyQ1hl06jM4BnQM6B3QO6BzQOaBzQOeAzgGdAzoHdA7oHNA5oHPg4uYAy5A8WTWJpUq60zmgc0DngM4BnQM6B3QO6BzQOaBzQOeAzgGdAzoHdA7oHNA54IoDbB7J888UIpkPHIB521aYf98N84njsJB1b3ZuwcFwb9oM7m3bwT2tC9xbtHBF458aX1+aC0vBZpiLd6C+Ig+W2jJRnpt3CDwCY+Aengq3qO7wCE3+U+lwhXxPeS7WFW/Gr6U7cKQqH6Umib5QrxDE+RvQOTQVvcO7o3XQ30OfK7r1+L+fAzyuy2uMOFjtgTp3D5jhBnciq85sIUv7biDpMXwptrlPHQL8fMTNjX8/1ToF/yYOmIxVMLn7wt+Le56jK96xCG8vPoaYxESMmDAUzbwcYc4tpgCLXnkP2TkBGPbYregqF2Au2IbXHv0A6PMf3DElTYyLMyqnai/evecloNtwDLh0JFqHN0C48TDm3vkZjH3SkDF8SMOwZ0TEhQNsNhlRWV6CwmMnURWUgPbxIacl7vAvc7HseByG9e+CFlH+zuFNx7HozQ/xQ04eOk55ADf3iXcOp8fqHNA5oHNA54DOAZ0DOgd0DvylHOC95p8iSDIfPoT6b79B/ZbNiAwKgJ8fLRR9fYCAZlIF6+uBwpOoXn4QhV8vgEc3EtaMuwzu8Ql/CQPMp/ajfu8c1B1fh8gwTwT4E22BnnDzCBDlW+prAVMuKgt2ojDnM3g26w2PpKlwD078S+jLrdiP2YfnYlXhOniG+cI3wB+eoZ4I9IgQ5RuJf3+YjuC3sj344ODnGBjZG9fGT0Fy4F9D31/CBL2Qc+JAPfWRfSRE8vYLhJdsTN9MGLVbtjoz9aOqSrR3r6Xh6X1O5emZdQ5oOcDC+VkPv4s8iuw2bDL6ZdgJDMzH8dM7P4j0vLxEjJ9sYdnmWToT8nbvRL4R8FQx0AUSpRuEEIKjdu7IhPcJX9TDAxU7P0AOR+7/HZu2eZBAld5Hiqurg0/TVmjXgDDkwKYV2M7wW35Az3GjGtToLf5jM37hWv6Sh/aDBzcIK5FghslogpmuVXUufqNR7EHvq9LdWPDlFiC4seO2FrUBaZh8aSood6Od2WxGvckEU20NamqqUVFRgQoSGhUVleLEsTwcPbAFJOfRuFTMeOVmxDdYSAE2z/1F8HD7UQteubOPc5o8wxHXhJBTY22fuxknM+IQ5ZwpmvJ1r84BnQM6B3QO6BzQOaBzQOfAX8EBt/z8/LNevjsj0PzTSpjmzkF0gC88Q4JB6g+k/eAMkuJIMQK0UK0rO4VjlTXwmkLCmkGDXQCfn+j6w0tg2vMhDOGAdwgJjoR2hgvcgj4LassqkV8MeLW+AR7xo1wAn5/ob48txZv7P4Ql0gtBxD83Lf8UPjJd7FixhPhXTvxzKzTh1sQbMC76EilN/72oOWCizd+OSi8E0vlVFiApjrs7d5966kvsr62tA4wVaEVDwcfbSwHTn2fIgfLi4yitNNMNmJzRC6HNouDf6E2vCeXFpaRBZhKlunsFICwiBD6Nzn+GxP4F4Mczv8KTn6yyLanbBDx99QCEU72O/DIbz84jQYjiYshjI5BQEqzPVqNuxe0jSYvVGiX7qrD6lnuxwCH+7CJixt2Hh4a0cJ6ZtJFevfdlSRDFEDGtEINKG9i8vDwMu/lpXJrqhRXP3I9v1XrFIIbraefy8mJwx7NXo3UI1cx4AK/e9YIVvx2sCHa8Ck9fCjz85OfOUl3HtZqMF0looxUmM3D+xq/wyeYiRPpRoLoa1fSorKwE1+NMXUxMN4y8fjI6N3MtSao6sBr3viC11qi7Z2FkUpBYB5j4A5PGeXh4ofLQatwvw064bxYGJATAAY5vDdHk073nyAFzMTb+8AtO0jskvM1A9GlNiyXd6Ry4iDlQVVaMU7RH4YnGTO/3oLAIBJ3JC9pUjuMFpZSXFgg0zXkFhyIqxH4m/nMZbCwvRkm5VAf6so/giHDSFv5zy9Sx6xzQOfDv58B51Uiq/+YbBP2wBEERoYCfryRAYuGHy0/NvKt1J62bUMT51qB8/hyUl5bB47LL/hTOsxZS0Ik5CI4JgpsvfckVtDF9DRTn5gbvsEAk+NXi1OG3UG4sFdpJDeQ466SPD8/BJyVfIig2FD4K/0gK4HD8UKbXjWhzI/4FE/+MxL+XTryH4toSTIufetY06Bn/HRxgTQKzhf6oOvJJNqm7c9+hYedBfyxM8vTyRL1bAHZVm2AhQYiZxiqDeFDfsvDwpJCFPRTPR+LkrsfyX7GocqOnmfFRijetkBJ86hFIY4v75umdCbsXf4y3dtYIQZY9PG9mAwIiENuuI7p0ao/mUbThvMCcuewglsx+AT8KFRctcTGYMH0a+reTtTC1SarfhCO/rsE3Hy50IjiIwbCrJmJYj5bOtTVUHBemp2n6eLzVcSj27dyEJUr98qrh6UaC+bxNtkIkrkIjZBY5OSdQPaKtgyCE9ZCa33AVrjJ6yII8mSf1hdjw+RKJt73H4oakUJDYFMbSvZi3cB35WmHCtRkgGapwpXs3YOG6HER4urIbaMKvC7+wbau8HKek7yyqxoADWRohEheRR8IZqSzb3zzsK56AVsG0sfAMQCwlOnQnbQaS9FgsQmJJgqyBuGF8B4kntcfww7sLRN5Wo67FqMRg1JGKVsFvP2DeKgmjuzy+tegCw/yRl7PKaT20cJI/Bq1aRSCkSSTCIyMQFxONsKAgBAcHIYieyilGh3eWisiEPzYqIr+BSG8RKN5v+evfxzPzdqhQzjwLXrjfqbBwwowX0D/ur92UOaOv8XENz3skxiNUEWjRsRu6deuIZo2XSDeehIYgayuwfcmP4NZoFdwNvVuFNQStp+kc+NdyoHhfJhZ/MRuZTubt1IGTMXpEBgwNjk8zDmYuwQuzf3TgUUzXCZg2uT8akLk75DmbCGPBHvz4zdf4cYdjJXqPvRmjBqUgqNGSeDP2fP8BXv9VWrNV0mtlyCM307Fx1x8OzoZmPY/OAZ0D/xwOWE8CnCPN5lU/SUKkqEhaDBNa3nHaCJCULahSkLQ5lULk9/VFEOclQVRFaAjcBw5SAM/Ls/7IMiFECmlCQi5WGxD0EWqFRifkCRh5Q+xGR39EXhJElfuEwSNu5HmhS0Gy6Pj3QogUGhVB7PPizx5igS1oUIDsnmKxTqzjTbsP8c+D8n5S8CUivMNxabMRdtB68KLjAPUNITCiivO2kwU+3N3pYRUwkd+L+ps3fdVn2RCnMYwQPnGY/JzPXc7HG3GG4XUHRUndkyPI1dfVY39VOTp4s2aOvNGVklz8mlF8jLYrtL5xvXHOQ07ODqxaSHvm3pMx7bLef/rCywWxDtGmgmy88Lh0fMshkSq14K2ncHDyfbi2d3PHZFQhc959mM3yDKcuDz9+/jJ+3DwWT902RGjxOAW7kCO9gtCy8xDc8UYv7Nm6Az6tusC8bx1eeHmeTHUqpj8yCXEBNN9V7sEbT30ohBkDb5iBEa1DQQoRJJgoxfcznsMqytGqXZwTIRKj8kLzjp0RXEQbcLXbkafSiNmcTG5Uh/ZIjOdDbKTlkrNLimzVjoQireBLmjCk/IKcXbOleBe/xdnf48N11FnZpU7GU5NisfrBFwRt1Dtx91PTEUnFursTMtNJLHmEOi27mGGYMb0fyjfMx1tLJGFJ1xsewvhEX9KukUC8gmRBiHsUxr38HAbX0Hdv90osmvEMuIu0GjUd0/rFwWyiUerlD6/SzVLGiBh0aN2aakXOHISNUiy6dOyElgYRi5ACJVZOtHsEJffDIw91FOOfNYh5bHt4+8G7/jhmP/66OjbH3v4Uhthop5Qje912GMMNCA9ppJC3LBffy32+61W9rEfV6uhL+Vk6hYdnmf1vyNb4ee9HkrmNnf44hrSLOk900kZwzRdYs+cUfKK7YfKYzo6CauoAyraQPgfq7gLmgJnMG3wxfw1O1fggbeJkfUN/3trKjH0rPsDLC10Lt3esmocdq37GDY/c40ID04TsxW/jXcevTILKvMwFeCrzIO574Vo0/5Pk4MV7VuCR1+X3kBPerFv4LtYtHIgZL49HnDLoncApUVUHf8br8jtMWbNV/vMmYKU6+lPngM6B88CB86KRZD5yBPX/+4wEQWTDh4VI7BQBDW81ecfp4OSdKW9Lxe6WnpQ3iLRrSgmXR1Iy3OPiHHKdTYT51AHU/f4eguNosatscMUu2BVtVIpKM3uINt4sU97g8CCUEC4EtyGbSS3OhhyHPPsqD+K1fe8hKC5UEiIxWcwThQblaZ+TaRJgFiLPTeRl/jGu9oFt0DKguX0OPXyRcID7Tx1JgPJJlZn99bT/5FOSoiNTWO46oou5kcaRhSRGLDxiOBY+0UP0KaGfRH1MUTDiJ2socZcUvxxmtBTifN6kHVJPNsbcpcJEiusfi3XfT0BdBwxFGO99SYDAO2NTZQn2rM9Evowgb908PLUuCzc9dgtSoqRNspz01z9M+ZijFSIlD8Xtk3oj2r8eB7cuw3tfZQqaMue9gCZRz2BEK1sDxAW/LrQRIvW6/Hr0S00k7ZgaHN69Du/NXy3VKWch3vmhBR4c0fKvr+P5KtHND63S2qG4vBrH92TJmi8GXP/INWjbVFq9Fh/YJ8cDTZo0Jbt6XvRH/bDgD1lQQ7Kb1s2kedEJXWW//4BH3l7uJEWKWvLuM1hin0q8feZBx0V2tanOoRzTiU145F3lq3IvPHhdL9FXh981FqteYRx52LzPiMkdPbD7j33Y/t5HWC+Xd/nkQYhlbaPh4zCAFuHcspkf/oaxb46AVdfDaiPKzTtQNn3kg+apwDray/gGBiKQ7J2BeMLOWCKNOh59PL5FiLRTFSfiKF44FVQDqwDyk9qnabSMWIk3F2Dlh1Yh0tCbHsNg0kxhvMIZ87H4pZlYLgbnPKRe+Qhu7NFUye3yuW/Lcrmdu2JgWlPCV4OD2btRGzsGDz4SQPOBGe4k1D7646P4SGbg5bc/iFSaGOrNldj+wyJUpV6K3nzEjYVq5HxDfax0uSz5Qkqwm/eGXkL9gCc9clR3lB7B8vXWDezCtx6H573Po/952W3W4uRv67Ajl8qqbocJ1J6km23rlDbmWPKrbW4LpYcuAA7UluXT/CD1lVa1E6itHFrzAqDyn0dC1cG1NkKk1KGTMKJnCiJIslp6MhcrWEtJzH15+PCpn/AkzeXhdtXM3zTHRog04MrbMaB9NOpLD2LlrPfk90MmXnilCb2HRsB2hWCH7GyC5Tn4TCNEMvQai4n9OiKSVHBP5Gbh9dlLZayr8Ny8eLx0TboqQHZanPkEFsrHjLXpNu8abYLu1zmgc+Ci4IAs9Tm3uloWfoNmZIvFepxNXbkqu0znBTAYqzzwtpYXL7xLpSNdjOsk4cRtdzjPd4axlv1zER1Bohab42xUHhdNTqybZL8UQ79Eitg8i3j+4Qj6TziiI4w4STjR6SEV/Fw8s48Qrkhv6TgbrY3Fwk2hh59MoBJWCiJaFJo4jbcSLEziI3E1kdVgnE+1eVCB1p8XIQeMdPzFnYSfrLHGGkVyFxb9mk69ibDo++TlLShd6EYG5yU/C4WE474ldX0pyPkI2EOOF/llWD4WV0uSqLPaeKReiSnje0iaFVLJ0u/ECdi3bSVema0ICXLx3hNf4ZFXJqPp3yhLyt+8ApKoiMhMHosn7xisLiRT+l+DBwO9MXO2tBNe+voGZNBC07pQLMf27+VdMmW/8sGX0MOgfA4MQUrv8Xg+IQb/nfU/wYP8petwZHBLxP2N9ZUa4+x+zeUH8cUDL9LCORm3PDIBk6o2IrzvKLSLkutsPIjv3lutIp8/8yvEPjsZzUnuv2vlbDm+F9rHuP5sW5J/XIIzpGJAh2ZyP6rEzuXrJUFkci9cQobATCYvElAewWp5o96LNvEUq5bNWlCJ7ZqoYcXjFZWKu669BF/MzsHoJydCVvaBf8uBuL7XFnx0MhJRHjw4arCChEi8T2d3yfUPagQAURh1/5Uo+j4Paf07opE6PBIiVgV05naswtpNdFcEp1UcUPvkvqz18GxCux5yJXvyxLOxP1UnyJD3U2+ruC6/6xn0b2ntvabi3fjk0bfF0SfGmTz0JlzZCCESqvZhyUKJM6mThoj+bDySiRffmy9Im0TjoDePA9qw/KwMj67XolcrA7WnGb9++gQW8qDL3Irur49HU6vcTOT/R/7wvDfGcd4bdVkBNnzzAeavF7tVfDV3A7o8OPjM+oxThpApAUVmSE+a7nX3D+aAu4d1Ce+pfCT9B9fnwiCdjqPt2KCS0uvK+zG5h/Wjtn9QOq6ZkYCw556QBelLsePIQDpeq7zDKStdhrPif+oKAZfc8iRGtJNFTUEpmPwSrc3vmSkJk/KXYm1OBsbYfWxSCThLT/72Nep7KPmS23HbiFZincfoQtJH4PXEBLxB87iYkTOXY9/YNLRjO30uXM6P82XhlwsAPVrngM6Bi5ID56yRZKEb2iybN8OrZYIk7FB2ppJ0w8pU2njaOM1G1UaYRLtdr7BggdM85iDczvEmNwvd0GY+/gsdqyDVcKZB/NGPTI8gV4m3IVACcRAmEYxPaCDMOb/ArWwf3M7xJre9lfuxquAXNGlNLyomiwmSadPSqrJVplFiHwPKu3yRjYRJtOsPpqOBq/b8gqtj9yEpINGuVnrwYuAA9yMS6ZCWkZsQEAktI+4utE5g+0asMMRP0b8pWvQk7kpKl5JhOcwCJt4iMywrPXDf42QRR092HMdl1bO2EpXdOGGSrCHByMhGUy3lsy6LGSs50pZI7DIaL8e3xCdPKJvXdVi4qS9u7G2QYGx+zSgryMOJ46Woko33+oc2QWy8wdb4tdmIstJKtnsJD98ghLiyOmmqQnEpHbuhHZdvkGKcsgy//byF6sgFx+CmawYgjOusoSO6yzhM3LoO88XH4iXYfXwQeiiSL2MhtuTJ8DGXIyXa24FffrEpuNRgwUIhA6hBHRm0sjgwR1PgBeot27cJb7/yP1kLJQe7T/hgvGwDj/tIMaX/j9JzbOin428zTuLKuyaj26TXMGsQ3Q5WE4BI6owSz22AKWDEkX3bRVpMexIYXZIsDJrWF+/Czh/XUbvE4NrLR6EjabGxodTK3CVYtY5aq9XlGDq8FwKEwS8Nznoyfk6afIGBmo2BmycsJgta9W+OI1t/wJEqgveS8phgQK/mTeBRugPLVlCC0hdiusJSuAvLlv2mQQ60aBGM8kO78MPe30iwZUJwfGf07RSnLvKtwFxfqc72Y8oazsO3n39uzSL7ttCZqC0OsTI+h3gpoqo4H7u3/YzZwn6UFDf0hnuRElSNA3sLUGWsQlXxUayYv1TVHut95b24okdzElQTbhd4leicNWSvSjTgUIztFU11q8GWb+aJdgMGoFVTaRzs+2k+Vgk4GlujOvPVsoTbDa0GTIVlCwtXV+HJz+LxCn1Bl5tAKeIf8pTbgRnmYt5z845Er0k3oejAo1jOc0Det9h7si86UR82VpShslbMXAgKD3HJAzYQXCNNcHT00BdVZaUkMq3EiUIun3AWFqGwtFQc7aRJkGBkQS3zW/Ccu7LkN1cV4/DR4yivqqU504PsYDZDfFyUy7KlhqC5+ATZBTtZRB8YpBinc7GUJH6tNAcQPTT+TBXIP5yPovIqMVd7B0UgPj4OgWfY8PZ4zcYyHD6Uh1Kqj4eHPyJiEugCFs14tyvXn46QtnBaXzMq6KIEMltG665wcUFCBY2j/Lwi6f3j4Y2IZvGIixKiXk1N7bzmKuQfOIiT5bX0rvGAP9XTEGMQ9TTTZRillRRPumOh4YFinjATfaVV9ag5USTaiLEVnTiBcv8AwWvru8quHLugqaqMLnmgxvEmfov5jtvsMPKKauHjWUc2QX0Q0yIeUfYMP6f3px0RF1ywFkXH8qQxQnb0enSKVXmskuoWib7jR+LH1yWtHouFtVi91eSK/dnYIgYZrRAG3ILhbTXanAzlHY1x/52IdbMkIfqPG3IwPPl8zmdG7NslvRPpRYdLBiTT2o7GskohLa3C2mLc+BTM+ooXKbRmKqtGW9acdeKM+evw2hL5LZ06CY+M8sVTM2cLSGWOcJJNj9I5oHPgIuDAuQuStm5FRAi9JHlnqs5S5FH99EW0rBwLt23HmrzjgqX9Y5phbFpHhCl2FRhW2cEyBOFinMWEG3HxIs/Z/lgKNiEinCZ4PrfD5fDkLtMm5nkRJ2OXJ35JZUOK42Sx2RZ56IcDhItxlhBuBLWQM5/d45fiTfCK8JduZ+OduShHfsq0KmRpS+A4okQGJB8HOC/HE/8YJ+Nu6X9u9HEJuvvncUB6uZMoSagZSYa1eQiYac0onpq+xiAeimCJqspdnLuSO8EwGIeFBpIIUBz3MYoTQ5bSOSz6HT3qqYDGLyw0CxvC0dBm1DOyLabeeSl2vLqISgF2zN+I4z0vs9FKKDuYhUXzPpVVzgWY5seAS2+chsEp0vEb07FMPPSstIhDr+vx2qROTjbydLvYug/w/NeSFsXQ257GaPpqaCKNjaW8uWOX2h/JwZLwTIpQfr3RofdQInS5iNiRexLdmxikRG8Dbnzgv6hk2zwBTeBHg5lZaOsoTimDEiSe2kJc0CFTGbJ++BKfLheSNEHqJbc8TQvqEFjqa1B4OAcbV7wPTTKG3vIEhkbl4eUn3ictohwSMD2B/xm6YtLYAeiYRPmcTYSM2XQC++Ri8pa/jXsklmvYk4fZM2dowrI35ys8etdXjvEcYxiPWQ/019hkqsexzUuxWuoKzvPYx+ZlUj+xfpW2T1bDhiB07RirKUtJ0fQL7iOa+qte0sC6tGeyJICto9u2Fq0WGlipAy5FcrgkeSzO3YjVO/LFGBX9SEFv86zC5ndn4msC07rlH74IB3bKAOPvfFrSVGLatJmc+CsOrsfrS3OklK7hKNixEVm7sqFEDb2lvxAUVh5Yg1cWSnBdp16NDmHWseUX2x23Df0f3mCCMmdjcecEXJZCdhX/cU7DrwbnvTB06dMVy+dLfaikupb6QD0yP3oI8+V+eP0Tr6ETX4No74wH8cFDL8kaCZfg6Zfb49OHnlc1FAR43nLMfEhuXW1/VzsXfTTzMWLvxq/x6pzV9iXQGBmAe24Zi+ZOtBhM9JHtu09fdTFeaC6+hebittJcbEVsxOa3HpX6IG1WHxjigU9fmiNpFFqByGfApDtvQq+W4TaxrgNavONxTz/gpTe+dgBPveQWXD+8LYqzV+KJ96X3jA2QoRfuvGU8WoZYpVimwq2Y8cSnAmz8nf+Fb+b/MEfWItPmNfSahJuu6OXU1l3ZwU2ins6mlktufABtKlfipTncB5Jxz4u3oznJu46t+wjPyu8lpZzlH85Sx+ql98zC4NMehSS+fCD3peSpeHZaDJa/8TxW280BjL/XpDsxvldLVXB4Lu9Phd4L90nqnywZFE6adzVDQkmgbYr1y470flZmQRN+36gcGwP69Ei0mbsVBN6xHUArBKnNMnfh2OQujlrHZhN9JvFyfYOryQizl4+TtYsnolr0QnL1Sfi16IMYb66HUrL16eVjFZ7a1sEKQxJn/DhTXitR9PXjeyHStF4FcJ1PBdE9Ogd0DvyLOXDugqTfd5EtC5Ji8ySlzFSaCauYhEj3LFmOklqrMc1FBw/h5/wTeGnUUKuRTs5Dm1MJh5vAaSHcuHTsObHfXLwDAf40WQr6rKgEqUqcQreSrIaZICkrb5yFk+vGOIsIt3uLSXLC2T220VdsX/4KQHh5QlbLYL+Ic42XQTQUigDjYK0kX39/MO5rYs6NPtel6ykXMgfEy50kPMJsCncruf+Kfkx+Pupm5v0HPYVgiZ4sTGLHoPwBWREYiewUyf2NYRlAkfuKOIoSKxlK53DjFxYMzJnZyQs2KeD01y+xO8YnL4K0fl6N3GMj0UQ+EmbM34yHadPh2uVj0ftPo+rWp4QwyDO6DYYQ8ArOsH47jo7t6MTYZBl2blSW9+noGE+aklRBS531nFFGp+bw5jgnBQfHtqKl/3Kxedux6zBqMqJlGwSeCIuOVe3jME4HV3YQu9XIcAR4Ol8IqiAXkofsR312z3PIUmlKxtUzbkR68FEs/vQL7MrKttscJmPq3Veje3M+PhWG/868D8u/mYNlWbSjyc/E/Lfpj1IMKelo3yIVvQZ2st2QWbwQmpKCdLEo9kZgmDfWrFijlg5DMtIZdy1HeZMC00FkZUu7JUM63bojJVASpZUXUBq1eYSnnWCT7Nr4SigNGeNxaUfSQDqnoyRGbHvzA2xglAG2ZZlZ88dECk/uVaiRNzTVpBlXWVMpjG27+wSSYFfuMxEp6Nuvu7TBM+fjkCxIatu9L3rJ5+9O+B6SBEkNjjEfRHdKJn5T3Q0GJEfQzWw+QQij29lwcjFWWBuTCM7AHU9dQRtq4EDmChzySUE/WUArccju11yI716ybkSQOR/UpFZnGIP+bcJQmfcrXn/JusGP9y9FJt2iV1hchJPH85BP7SK1mpR19ftzkfrcbfSxxIrqn+GjtlOHfMPzns3cwPMMaT206U8zV+4KUdUtu4+hYy+DQ7UrDv6uCo1SJrZDsMWdLvxu2Im5jUE081nmnFfVI44OufNX46WHLXjoVVuBvjE/C/c995kDuDWC5uK3n8b+MXfgejqyaxWDWeBLZjZFI++Yj2etMmhrVuHLx/xXH4M3CcrSFS1POwjboBbv13jJBd4dS9/Gm4dTkJudbZtdCeWvx6sPh+Cp14erx5TrjdZ17devPq9AOjzz18/HY0Z/vHR1J1UYw0BlOSvxyJuLHeCViKXvPwurSMJXmpO4+3g23Jqe9IK36TsKQpundU5D7hw88IBNok1g/fxXcaDoP7hndIqg/1zenzaIL8iAHwxtaS7MpbmQRtHW3QVI6GQvsC7DxqVWYWOIL2msKnMyrZ6sdwdkoKWsaelY1WC0GkkrhGVcTiYOnbwCseoRd4oyFWPlu49jceVIPPrf4Yi0DhQJVcU+fPbga8jKuBrPTUq3+xDhhuRBE+lPLlUzpuUYephJCy5PDiYjOtRPUwcr1MG136gCypTx96FjmAXGfHUCk+YLte7WfLpP54DOgYuDA1aR+lnW11JQINlGUvNrJxhgEWkiaYVIChjHcdp1/Xurm1xpdcU7VXJkdJJxyyEp7ix+LVXH6ECwppoKefxU/A3hVWCYEPYrBNG16QJ3Q3kbkZZfcxyetGlxcFSWzdxsEyBoWbLF0YIkhS4ZEV/rzrh1d/FygNcd2rWH0pWVbkyntUTnEfH0w31JHHmTotV+JdIZF2dgGHrwTW4sTBLw9FQEre6NGlSE4KxcIJLTUmhtJy/0WdolnBFZX89RAkgfMw0je7SVVPXpCMOeTd/jna/Flh0rvtmIvjN4IxCOTqTWveJrxpWFXUcvQ1zLQBUHe8yFB7BM3rka+ndFjPzxruzkCRUuzhCq+h08Pn7WzVtNnRDOOcA4RJhQfHAXvn75Y3Uj2H9aX8dFpEO+CyjCy4Bh04Yg6+MVMKSPxw0T+yGSeWcORKVGiGRIzkCf/j3QuX1zm2OH7oFxGH71DPQetg9b16/C12uk9s7PziJhQhj6Du5kW1mvphj9n//IcSYc/O0ncVROarpk3HrrbWilaVrzic0kLKL+kjIV91wtC2FEbhNyyCYTkdigi4hrg7ZtQ5Hz2++oImGSGEcN5rAm1vNxS49QtE9phfCrM7DhM6lfWiHokMGmj/CCnbZB7uI3MUPebyaPn4Gb2zt5Z2iRnLHfHa2GT8erQ+nWNs2kUXZwM75cxrIlA/Lz85E8ZBquG91J2GTKX0+3Gn3BzCLC7p6Jfs01TNaW7x6JtCHABkn2gZSMDGRvsNZ74tQ+hM+ItZ99bCMo+vqDd7RYnPhz8dri3/ASaRN6OUn9N0RVlJc5VCO8VQpo5gJzPvuX3aggQZIt503Yu40aTTgDenSIYakkrn3hBZqwK7GWN6e8d00eg8dv7icf7SStBzmH48OA8f+Ziq5tYuDvUY/CA1sw77Uv5PlpDQkp+2KSutEuxFKtECl5JO6e2hfx4STts5uLsxe/ho2tX0AvrW0Zu8JTRk7D+L7tEe7vgQqaj5fPew1rmHZyn63ehY5n2fYZE2/FmG5J8DWX27wfFCFS+pj/4NI+7RHiVY8Tezfgkze/lvvmMvx2ZAD6uaQ5HdPuHoX28eHwqK/AgW3L8dqcNYJemhCxfSQJvyLl3koC90VaIVLKGNx3RR/E0ZE+U1Uhtv/0LT5b4XwyMvS8Fi90o4spD6zF4zKOkbc+jqEtyOIbaQ67azRNpMIb+Wvoj1uvG4qkptSjqL12/bIIHyzOEpnz6RazDSnP0Thnye3Zvz8bScnfCta8x0gkU//krrbm4ydRS/1hcNcW8KdxVFN6COu/fgcr5H6I/v9Be6VNmWpzKfKUZkuJQ6hmPrWvVGCQVSBYJx/FV2Dyt3wtjVMsw5Mko7QRJilCJAbe8Bk29ErBYJd9UsFo+6zY9ws+3iAvcNAckbaTiAA2F/6Gl8UaiYNDMJ5uDtWdzgGdAzoHtBw4Z40kMiZBlj4DtDht/MpxNptIObAq7xiuc5bAcfy1l3BbpfyuAE8TbzpFBoRd02crrbHDJaQ0dhIaGcSN1TcI97nSV1Z3CoEeEZKQyq74sw7Sxp6/lpfVlZ4zfWdNg57xb+UA90szWdSmriCcEBJxV5aFPiwI4jRljcNJLAxiOOHoyXIa1lwSWkjkFxpK/KQ/hheOAiKvGpaMbTduXNBXU/7HZdIP51GKV9DZP4Mi2V6B9Em5qLQCllhvzoywlMHol2CiDVMihg/qKH2dY8TewWjTdwKuO3YAH/ORg7wjKK6xIJh2TYYOPWAR9gGApZkHMCSxg8oPLvfoH5mCJvb3SG9BgjOJPmOF1S6FibSTtLYRGFZ1nkFoEW0BnyoiIDsNFxUK+es/x3NfZFojNL6R02ZgeMcIlQ5N0gXtbdJxJJ55rA/8yYZLTVkRiipoTvL2QeqYDNRWNUPndokwRIbBl6b52lNkY4TlKxqJjJC3+DdB+pBJaNd9GMqqK3A893fUJWaQdoV9PzGT/ZcCHPrjV6ycs0wVwDGDJt53LZIDuG9Z2VVbZ5L4SfZp6hnXia2455lPrQDkS21rr2lmgalawiPd6laIHz6yGtW2yXzaQDLue/U2eFeclOioMKGO6OCezE6MAy3BUrT6q6SLMUbGtrfvbYJQHq21x1HK9SHI+VnbSYNLxKLwqPwe4DQ5XUVm46HrGkg6zEWbKo7g52/nYJF0NRGNG39MveMxdOfjRDKO6K5jMfjnHVhB/furlz5Gs6emo5WTY05cRPOBT+KxPmRbJygQXvX5+OLAeogTQP1vRA8aw1xmix5JsAgBGmlEpUYiNCiItIlDEBIchIgmzdCE+lIwxfmTPbO89XPwAo+Z9R9hc38SRsi3/9lU54INcBtJfIYvPznk6Iz5W/GGcvYPqSSMkTUGvOPQo180dqzh+WwRcosG2B5vMx5D5noZZ2pv9eitJ2vcUUmBokzy+vrCn27K1fY7QQU3hoamqTPuRHdZU4Kt2EXQGJx2ezVmvCZpZJws5XUarWHIVeRkYjXnZ5c6HjNv6CcJuThOzMUT8WCAF575dI0Amb86G12u6iILsZhm+idnTx55B24Y1lLAcVxARCLGTbsdR2dIG3ycrBDj9/QiVVu8qRPvw8QMZUMsvR/+Q0Kq95mf5AxD/g9XDWorlUv1bZLcF9defYxoXi/FaezhiLZTCCb901tnXiULrYlgzwAkdhuHu8qL8PIi6Z1VQXaNLLKxu+I/NiJTyZs6kXjVS/CKcXr6RaDLqBsQEfQhbeSlvFy4VB55yGabN1XcLcBXxHGary+ZSPD0ltqTcMhs5CQXzpYvVHE8dN9o6bg400Xt1WHQVbifLo54bo5U969+3o3uCVJ7ne370wUxF1Y02Ra95cnbMeeR14RG3vpF72O9VQFJpTV55I10HLKDbMdNjq41okjhv3hnuG4Lf816xrYvAdEZU3D1kRkQ3S5vKZ6YZZGESTUH8fkDVk3B1EvvwAAxh56+xVXCSRA179Wv1D4y/u7+DnYeaTTj57kfqeNx4n1DVBgtrVq/il/36BzQOXDRcOCcBUk8ifxZTuA+R/x/Nn3niv9c8zfEe8b9Z+JvqGw97e/lALc7awfx8BGCIHmYsgCJ9+uqIIjJpDShUcQwlCA0jegpZxE42K8KnRiGwvUMQ4IkNuQthE4CldTnGtfvJPoom5ST+6sUcPnrHWD9bHbweBks7cMIlo570C1gbZRcDnjcEJNAxz/EzlUW6FBBbmEtcEUy8CV/WVy/DUcvba853sZfY5UF/BCkaBZqWsFRw2PMhzY/hFvsT2S+KDRqnnWmUk3I1muqKUcl1Ye/Af+znBsC6NIE1BzAR4+8YiPc4XrQiagzcMm498VbkZEotbBN3zKRUOLeWdIRMS3G5P6YPmkYksJ9UVtDx09UNRsP1NZ5IJk0bOiaNRJg0bEXd2/aAvIhBnbJGHn1KPTvwsagtb3RG50mTIflj2MIiAkiO0+n1OMt6WOmoDMJMjQnHgUm2x/a9dWfwI+fLabuQJu/OguapF+Gqz1yUeMbb7MRMXSfhmfSzERyDTZ+/JT4Kp08cjqm9Y9Vj7bVF+yX0efj09deti2KQys+xcsOPLbYCKwcM1GMsQQ71y0nLYQN4ogbw7B2xrhBHdRNrshHBspNljD0uXwMVry+mKJy8eY7y/H4/cPUI5sCTv7x9AtGGN0SxjNF9nefSUOR9GruGtZeFdDG9r4Jz/ekmybpSEceCQm8/SJhYM0IdiTYWr/1D9QR71L7dEFsxiUY8kse/MZcix5NHI3VS5ku1F/NvFdYjVM1tQgiA+88h/IHoHoa84d2bsK7X1ob0DAkA3F+Sj43tEjvAayhm23JZdodb6sgjUZl5urfva3d0VsFB2V0tcnVzp8pU9DR2WUAccnUeqQRRX+5O46ism8CzVEmHNj5G8VI7ooR3RGgxSXHN0nrj/4kSFrD4cxtyJ+QJuz+8BvHOuSSMaKPE9syfjHoqiil5u7DiVrii5eM2OVDizcFg7rYG092QxjPB9JEjR4dW9iNfTpw2yyB0iVhinbO184RyWNGOAitmSRDG5q3ZEHSoUOF9PGDyzJi/7Y1nCzclBFdnfIqoecIpJMgSdIJ4npo60KjycowSmvgo4ZSkM3TFtfIy/ujiSxI1oJFpw9EfxIkreFITXud7ftTi/tC9p88XkB3cDbsKksLUVJeD79AZXVE8MRDbzWb3GZq2NbjFxCqRmj7lRTph7QrnqV2fQBCcTWfNJPu5PGVr+ZJGTMd1w1MVOdQNaEhD9kUXEJH4pQ5InnMXeibEODQ5wt/Wy6bEWDh6s2ywF96J2r7vSPdDRWup+kc0Dnwb+PA6T/mnKbGbsG0WeDPx56az8maPGxYm20iOXMDY6KdRUtxhFPgdg3RqBQ3bzbuaiKtJOf02aph2KFUzuvYRXPQQtecM+5zdaFeIXTLBhnd5WtctfuWc0HMG3vCybh1d/FyQBHyCA0i6hO81GGbScptbdzdKFrEsfCIbSYxDJnYogWqlMajhsNK1+SnalOJ4xkfxbEwSQBpFrYUc96dqdZqnygmwipUUgoyVpTgZGExqskwbS3ZmqkkGxY1JEg4LtvEUeCkpz/a9e5PO6E1FMzC7qPj1ONt2mNtKZfRxQBShjP8NVHZp8/SrMtluM73COpJOwC0VS7cm41lG3iLRvKAuW9hxZqRePw+2qBz47h0Jvz2xcv4RFVVdwl47glkk+HFiV0ad5yIzkM2PAsZ2CQPrY3ppiOXlPlKncxZOh2j6zMlHRvmZpGtI80SOzcHbz21xlkOKY4L3TAXj9KfcErmlK4Y2KW507r5N21Fmi+tJHgSuCguvm0aOsj2iJQ4p09zOLKEIIlSedD4G9CFjiXZO3cff0gXxnmR9oiU6uvrB3+KV84fmUjLNj2ZdtR8s5Vw3mRwZQOyJGmYsCelNbfBG1eEBbg4hmdE/r79OHLwD8xdvEbGRw9qE6QMQavQSmz96UcUk+ZYRVE5yoqKkMtp9o42Ou//2AL3D5N5ZJ9O4bLfV+CDNVLeITdPgDglQzdWHdl7DMEtWtJRIqK0YBdeeYWFJCm4/8UbwKw1luzHl99wXDISenRBmE8YRt1/v5MSnEeZSOvs3pmfOU88z7FXP/g8ujRWQyp/MZ76LwviGnL9ce2wtqognyH9Y9qjP77BGvJnr/tdc7yNjrX9qhxrS0Hn5LObubgMdiltDUqXkyKUX69AxFHXFdMq9VFpdUU3fhUp/SId8VFK31QyKc8wpF+WQnIwnuN8XPRJX3i7WLIpWLhP0zLsjJ1TtOprJQUJEY50m2lePp3zJW0gZ05LorGOxYWSq6EhKTkShtFtfE6dVxQdBaW30wanqecxMgWtYx3fp6IAOpqaOiYZa8R5SGov9T10/t6fF9r4zN/8BWbNtTI9OWMkendOQoQfafqf3IdNNI9z783f8A1mbcjF9GemkSaazBhqcLVpT9NCRtK0bdj5o8vEZ+lb2cf4bA1P7sr4YgH/dEwd1MpmXmgYF6eW4Kf3Zkq2ITmYcgWmDWrOPltnPIiFn6yR49IxZYjt/GMLrId0DugcuJg5cM4aSe5RUTQ3FdN6QHmJKjtKYit5x3ROFYa17e0khXn7ijSxk1VbgPPKjq5FdiPc5nPcmLr5R5Oe/n6aiOUXtUKeUpSyQ1bKtX8ynAKrPBnGVAc3/3gHKb599tOFDb7NsJe+qnvTsQ8bR2VxcWr1XQi1RLSWLhlJHdHX3NdwzvTZ0KQH/jEc4K9EdXyDGhvclvuHWNBSfxd9Su5fXCG+nY2Xt0o8H2FThEUiD8HyEon9DMNCJkVwxPEcpx57IylV479Qab6wEg6Rj/A15ErycgQcwzShK5U5j3BVJ/DzkgX4er28k5ZinfyKgiiflBSa3BkdLKvFonBp1gEMlo+35e2xHmtLT7EdR+401ynlepKmi+J3KIyEDTk5VEdOoOHtqn6eAdHo2I3mKcWldcOAYfvx7aOvSd/ASa39q43tcENGrALh5FmPypPKlcVOks9n1IEK1BIDG/UVQrBb4sH4u5+hL5/+QpApyKHOU7Y/GzkkkwkOa4bE5prrxImvNUd/xgMvfE2gtm1mX5XoblMws+MUsl9xHJ/fNQtZQiiUZw9mG86zS1eC1XWNqxuNCaU9v37uHmwU0jDbIuxDbGNIcg3Xx5rPOj64D2n7mWeTjrhyekcrKPvMvVFD9ecNTu8RE5HhTLhF7ca4bJy5BCtfe0fWerBJEbcOzlE+XdslKUEDGTMPII2kXKpe3tI3sbL1MxiU4LgpNZf8jnfeWSqVnz4R3ZtWIGvlT1hJwiuyvoS7n78VwdSpqkurpbqmkJ0z2ci8hW5IkupPG1mug0MlFGqcP+tr6RjsmWZyjuq0sSXUh7Rai44ZrO3qmGYbkzLkakwY2QUhwniyJs0tAp0v64DVbL+EjrftLe5PRnBpQBmPW4+19eqOOIfbmjRli27opD9o4knJxQXf/BASTnl53JBBeGkskPFmpW1SEhHVwAUB7iR0kdojE4cLJ8hGhjW0uRzztjBqeRrWOHpt81DB/N/GUYxMD9XBntcEyfDW/qPA2sYzSiuMFb02rxVGy6sEtZ9bcyk+TzSjo2SW9VmMXRSopV2Lm9Odla9gcnxK8BK+hi908PWVLpoglSScKJ2IWNkm0Nm8Px3poDXFhTQ+K3LwGWlgcVuxm3jHk6QNa/0cEhubgPZpPbB9ycf4WBhK2oE3l+7Ai1d0lD5A0Ne6IGKqyN/Ae59xlx3fr2kzV+1HmkkDeuDT1TmcRXYp6N8n+cw0kUA3c37+GBYpaAwj8ch1GU5ujTUjZ/l87JA72pCbRyPWbh5xow9ESl/zVOdmhTb9qXNA58DFxIFzFiSZW7VB9ZqV8AtiO0RidS1LQCQ2RoQGi9vZ2LD2arKJxG4AaSJdmtbRemMbR1JW4XjXS/+rq6tg7p6hTlZy6pk/Qtqjsux3BAYKvXrxLmYkXIzyolALlydOdefNNDE5/FScHFdZZYSFcCuTqZJ8ps+OQe2xs2ov/MnOlBsVJPCJ8viHhAD8ayXUBr2gS9BDP/wkxzjYX1NVBcZ9rvRJWPXffxoHuN1ZGET7cdHPRfegfsTaRSJe9rNwSHQZ7mOUxg8WEinx3Pf4Cy6HWWgknEAmwCUBEoU9ZHyczmU3rt9xaYqTF7VK0OnTiCP7eJssORNJtEQ5dCvUkgdmYqWSwE/a2KfQzVM+ZFMlNMQfh75fKR9dUuiTgf1iQdMMGf+l8PpfkTemPWJ96La2X+Ryki9DUqj1CnLOFRYdI2cGjhdVwkJHP5y6OqNVNZ7sMmnt4DiF10R6BrfA+Ievw4GnPxHfILO37UNlTzJ2q4Gx9bohsvMYjEgS9xTYJp3HkIkE/GGJbZ0sPl0Uopm8JGEA2+GRYan5yw78QlowLPwbg5mvDrQeCeD+S4tVxUl9SgnZP+mWT2oCi7Fe+hJMAo0xtz+GPnEaoVV9Of74bTcqfUIQn9gSUWRnR3Hu5N277H68JzqQfPRRSXT11NSLQaxCIlcZtPE1oi/YodACyH7N+CDg044pAlc+R1jEZliT3wl2NcqtCTLGpCMrq4zGTAiNmQAxZrxoW+Tl50kmEMnO1b5P8A2PERL4XHfvRLQkI7HuZJPF30eeFWgMfnv301hLEItf/hYtZ14JkhmqzlSSiy+eeBf5QshH0Vlf4CneG6vOeiNVZfERKVYzZqj2KmTDfUEFs/G4+TfDiMEj4KVpdxuA8xIw0eEu0t5o6vz2I2sR1rqQXgGum94d/nQukmQ2qvMk7bPIJgaEBXI/5bZXk1RPbId0gAVJ5DJ3H0cq3QpZcWinECRy3JhuVrtuHJacFpHUp7QxAkZbmMt+p82l4NHE0XWDLGx2MTNylWRnIAGwMr+qkTIZzurdGBgFt/JsRB4NiLP+RZQoyMQ4VMaiNp4bSYlXgcnjHMaKD9knxfFl57wiLa+SMhWdPW1a3FKaBq+ay5VHC0savHTcli7AdO7UqTgZfp6ajydn8f50VsCFND4L9/4q3rlMZ8aU+9GzhSJE01Luh9RLpmDwriewkt43WJ9Ja4dUJPAE7BmOBFIWXc9DM7sIp2id4nDjmoyqVr6Vk4Mu269iP/736GdyDuWRjRde/gEP3zvMJW4FUnqasP3bN0CKu7Lrh/vvGooIJ0cZzSU78NYKrhQ7A5r5nSJt1RLxsVFE0emOqsMHhZd/jhz4A0dq/enu0wDEJTR1qs2rAusenQM6B/51HGjUR+WGam3p3BlFixbSF4oI2lkqW03aKfBZGfk9FR4SBL6d7TotImUzwXHCr4kgrYaiMvqCSLjP1VkiuqHowFwERhIxYsfM5Ui0cVAmUfKoOxyZJkpXozibEiD1i6LiWlgS6dqMc3S9Qrth9tF5sETSUTnmn0yelSfOhUmCFIUmfrITYaoK268oqkKvDudOn4RY//0nckAsTIhwHpUsCJL6lPyQ+wyvD1kbSQiXGIacgKUnp7FNJXbsF92LfvjJ40YZOwJEiedzc3+SMx3bqlkIZSBZPj5SdWS3VYhk6Ifp04Yg2e4KksLIUjz9ubqK0lDojsTuY+iYEx8vodvb8ujreMRBfC+vozJ6t3MivLFOm2t/249RHZwf86o4flAVXqWkWIVAZroN50RBueB7UJSBTicp86aGLPK6kxBMdXS9sLqWVyO1Hi8kZwykbf6F7KzHOiQqjcg7IGmQJY+hm9vOlXRioyJI8fT2p4s/lRAhNhUg68tvxCZ7zF0zERumSaPk0HDalDvXyTktVZfd9TRpWgWSplVj+77tzWgNFaAIF3x8eYdnwrHcg3CPicGp337BHlIEpstNrc5Upm6Avnz2C9SMiKIcVmeqMqFlnyFoK67Qs8azL3HglSA5nuyMOHqoFLG0KVCcOXYKCZLmUtAXEU0jwfINc8VRbN1ZjqTUtnQkLRLD7r0Ma1+k42cG+qgkeGHt1yd+XS5xV9mfyIgNKf3Qv3sa2iXHycf5zKgols+D0m2rDfd5hbrTP73CkjFs1AU4Ouj2vnbJyWe1+XIPa4nLqErf0BDi421VJEg6+Ov3MjP6IZUEqQ06+dhkgzBnlKi59jz3AIrofI90RNMeCbexIhyJIIPS2k5sD/vvDStjG9iN42WjEGZVetFUuhz7t0tzpCbShfdcRksB6O4BmlCcoab1eJ4ycH0R5Gsd17y6OPP3p2MZF9L4NFWWqwTGxoWrfkePP5rQtkeZdJ1zfyX2F5Cwp6kzxlZgn3pFKB2pjHIyXlmI9PDr6pspZcQUtK1agy/XUnvkf4+nX0QjhElm5K56H59wHuHScdfMcYh2RhKlmyorZTh+5OPzV6iQBtyGLz+UbRQm465Z0yVhWgPwepLOAZ0D/y4OnLNGEmLjYE5Pp9Nj++AVwYbjaEcpvmjxzlIS2AiWid2mE+aJeDmRpSP031R8SuC0EG4Jl5N8jY0KbA5LVAaMpb/CJ1zZmHF5Em1cpLIhtkHJpDAYO37KtLHfWEJCLsJpIdznSl+iX3P0C8/A5tKdpCoeRsXQ1zmZNlGu+JGESUyK6rQ0cSSFFW2kU/QFi3EybhYm6O7i44AQIrGdMepLvH3nRQ73BO423CUUv2J8W+kmHM9CJQZQtsVKHB9pY8daTQyiIqF4Hh4ML3ovIWtcv2M4RkSOPA1q7FQdxTezvpRg6TdpXDc0k49OFB1VdLWBcZNHIIlsXNiWb8Sh3YoQSaJNLZdw+cW2Q0+6wnwj+bfvzkHr6D/kcpLQJcnxxjTPZkkYTBArGSprIw6PS0OiwxqQbJVkWm2fJLWIVGk6tf8XzJLUX9Dvpscxto3THQTxxJ0uWZbXqbTKb5A/TMuF6LgvyHSJPqllvKkIioJZJGmN2bYZuyZK7AAAQABJREFUdwk1p/CrQVf1tCnL9niRufSEqqkRQII7K24JmRpmHBo8rooiILVeOdvoGMReTxuNEpf55IQ6Ov4U330A2mivjXbI5CY0/Tg6a+6zyF9DfYH2Ah0m3IS2+7/Hyq2c4splYbEiU9CA+HWmMiO8NTH23ips+/ItfL4xH4Z+V2H62DQh4KslO4OSo3qzJqD7Ccx5+EVIJPTELY+OQ3JML/z3tniEJLLhZW37kRafMJROG2JDB/TrmIykFomIi2mKEI2GkNQGlTi8W9o4d4iLVg1Fq+1DXBftw13DXIFD+4oQGh/nUhgrE32BPbgOMknyzYFW0fSZkBqAtr16ko03mrnyd2PfkQTs3iDlTxrd2am2AbWKpmwXWkOavi3xWiFWS5sGD8OLPH5ompRE9OwlwGxk7y9FvLO5zZiHtSulNuaLEiyqVoQGp7adtcVq6XcJY5OBAqfH67R/adBY06V+rYSVJ4OyXxtWsmvjrDDeiGvThdjEIygfP2UdRJuBCUoW9Vm1P0v9qCHVQ1sXqUwF2GSkj5uWhsa2Aqk8tbhysf7342iRZhUeK1AwHsYaVQihbS8J4kzfnyreC9Sj5WF5OfG0mSue1tHJCaUS0vqFugA5byR1oxVCtlghYMvOw+jaJFEBVJ/mwj1YnC8HO7RBU7vjY+ay/Zj7+BvyHEvz/uhbcO3AZBLdtYdP7UM0R1NeWZj00J1D4epVcnTDHLwltH65rA645fHJiFcN98vlax6szXp2zke6mfZss59doXounQM6B/5mDpy7IIkqUD9qDI7NfArx/vSJi66UFbtKMaPyjpNmFVcTi9iNih8pDzOjugbHKuhaV8JJb8Xzwp76+CuQ/+tWMuxZCzcfeimo5Uq0CYGRTIZDgRyvCJHIa6EbVvILzTB3vuK80Xdl9BXY9PtWGP1q4EP8o8MfvPSR+KbQ6ow+JY6eihDJSPyzFBhxZdsrnC5qHOqnR/wrOcALVu5F3EWEEIm7E/cTubbK1zNeM7AASHxj5HQO05/iWKjE6SxE0sIwLqHJRGkMzmmiHNJEsC6WKbJBx71cXszSPfA0GQlctlnMOJm7CV+9vUDV7mEjvGO6We2TeXnQLVryXFFUeAqWmEgbFIW71uCzLGulJPo0IHS0p9uoJGz4Lhd5Kz7EK0pSRi/EOl1whaL92A5Y8W02Qebi9W82Y+bUbpAPz4rcZbmryPC1UmY/tI22HnfxC7MKlda8+yO6zCQtKG1mgcGEXSuWIFuZAw3h8HHKH4XYC/RJNBtlurkqSjsxtSV7NiGLI8klxFl5IiLoR9uPWIgmgyrJjk+pANGHJK/CfzIjs2e3XHY6aSMp9lmsKKzwXI6zfmiFFT5NWdlrlqlCKjuoBoPj6Gr21vJV4FZAM6rLinHsWB6O7svB5mwrLYpZpyAfX5LHXI5RcXW2dqrqirH5u7XiA3lKv9F0Y51WPEFHpzwN6Bpv7YfWMhVfBbYseATzNkjhvAq6SUzmhbYthN8thK4n74Ks71hAuwFvPbEB4257HH0T47nhHMaxX3w/zJrVmzSobD+DWyy1KDyUg3U/HUT3qy5BdNURZOVJ+ZvH0EUZzGdyDuVzdC0daX3rVRqBKbjnuWl0JFWA/gN+uH70T1SNnxw6OxeR3AVJlg3Eg1x8+KIinAF6pNjfOqjgt5CWmlw2aQ0V1loQYNskov2YOIk8iT4lt/XJ6ZzGMRIMew103M6yTKJjxbvfoQ1tWhNDxJtDzlqN7cvmqvOaYUQ3xKi2lLQ4lTaXs6mPxsCowLLn9Hm4HrZ9zRaHbf+zhxVM4EgVhza3Nq8WJqZdJ0RbssR4zV38Cr4NuQdj0mKl9ywhKDu0Be/TjYgydoFSwmXFzrc/KnT/fqQEA1qwiQlbV3F0F9Zu3I0q/6boPbA3ov2U9rDlS9ZnX6FDzPXo2EQ7kKqxbdEXyJUaGobB2vaSyznj96ctfRdaKCwujngqfXha9tYytHX6fgYKd60ibUCldZrCX+3HpOGa1J7G5QoalbRCWPw6NifPRDebl3wJVnzxGXcH4TK6JKtCc4UfRXuz1PdjyujbcO0A5XY2P3Se8Az1iwfxOc/Vecuwp6AvIqK17SZhKdy+FC9+qXxES8FNj09DUrC1/yplaZ/ezdLxzJMpYn2njVf9Hl5kv3A9nnn7OxE1+KYHMJjOMZtInS3QThim5tE9Ogd0DvxrOaBdaZ59JUnV3jJxMsq/WYCgKNrE8e1DLHwRsyTvThm1MuEqxYhIKSAkOeQlOwHlJaUCFwjneXOBCbAkTcOp4+8jpAlpTdEZX4kmpkGmyxV5GiES3053qriccN1IOtsJ5428Fn7xuCV+Gl4v+RgeURHEPi8iz16Y5KI4Zq9MY12dSfDvdsLFOHV3kXOA+jQvGWmtKW5a4eHIf9xdWDjETmgfUYCflCSOsrFwiWE4rCx0GA/f0MZpikCKQAR+Acfp9MdxZ+Wys7D65xo6OCM7nkMqSrB3+0rpZiAlns7sT75nss2mMax5AqVKu9+1s58BJtyA7klR8KivwN7Na7BgLQt8GnaG1B4ACZK0bnSXls41/QkooWt/JJMgSeTImkdLujr8d1RnhPvWo+CPDXhptlUdJOOqDBs7Bl5NUjAueR6+FZkJ9sECjLt2OFJaRpO9EKC84BA2L38PKzVkX5VxprezaGvyd/oVkSX1Q+3JNtMxrPxwrUSYYTRdm12GP7YdhjfZn4qho1N82s+Lb7IULgje3AFP56gfKwdmbMqq3o/FC+TFdHpbBNWV4WSZWWiAWk8V0jkcdpV16tiQIk7/22/yTejc1FtcXtowtAf1yRNY+vY8qd84AzYewZuPvyo2lzbJhhSMIOPJnVKT0EScF0oA93obZz6GI7IgqU33PshwdXbBJpMcMJ7E8o+exffKEEgZh8dJOBroDFbE+ZAGxVQ8SpuuJ9/+VsR8+8bjKL7qvxibpjEcr+Z3JyGStRGNZYU4sGcHNsz7ThbCpaC7Bx3BWPedXPdkNI92XbpAe9obAdXC/70evzj0IhtvubLwT1SUxlMrV+oJNGOHBCmGqrLx5efLMbp/Evz8IpAQ7UIz8gy45xPdERNS5mGBmLuy8Mbj+Rh97Vi0bRJIBpWLkbX0Q6xV+hjN5aMzWp4B9n8ZKNnYHDsiGW/Lg27t5y9h7aZ0ZER5o7b8ILKc3jRqywOvAKvgKPfbeVjqOxodyEZXaLME6UJHsl32/UvK0SN6S5qa4JWxbWyRqKFczH52BgbTfNYxjvqCQ3sBI12015m+P9UiL0CPj6EdaRx/Kx+Xl9/PN1yCjolNhM2v+poi0vxbjnmaF3T65DSbdzx8EjB8dDJy5TXFvJfeRM1NV6Jri3DUVx2nee9V61yLDPRrZ/vxi9kSmTYed9Ip33U1nTB5YKJYa1nZRQa4J8ykl+Q8FCaPorneUYjEH7OemS1pRUn5glC8ZwN+rqmzopF9dbT3Cm3eBWnCqLgX/IRtNgcwNcJHuU6UYsICya6en496rFwF0j06B3QOXBQcOC8aScyp+j59UVRKy/hVyxEURsIaP94SyttKsRt1ssXk3So7fpAmDQuRigYOhZlwqTtYAXDuP5ZmQ+jMfglwcgGC6Yibm6+3hFTZMTshTyGfn6yJxEKkorAJMBOu803fyIjBKKIX95yChYJ/PsQ/N96x0+5c+eJkzwVFgMT0sSYS829qwFgwLld57HHo4X8nB7j9fchqNndvH/oTjvdx3J/ooWzp+FlHe31FM8mTYQmA84mb2JS8UrQqcOJo3oSzppJqGo3iagkXl924/sfq4JRJuFx8L0lWlAjHZ4fBuGP8cCTQF24tfs+oVFzZZR7+Jx/1WbuANiuOueUYrQq6FcgzIhmjk0iWxKcyhOuJ9gkBNuUoKeLp2wLX3jEBD722QIrOWoDn6c/eGQZfj0s7R9nh8UWfq25F7qNvYqfIkItvZ9OffWY5PPj6B9E5Srm1ygXQhRrt2QxTnnwMZJ4H/iG+gg98lfsPH70pi/6AwUMSkPP1k5ivOarVgTRqurRLxmPPzEKIH6tMcJ86TSW9m2Ds/12DIyX1MIRLZZmrSWD1yRuq4GZ0ewsWPvi49bhAz8HolJKKFslDcP01aUQkbRa4/56mKCZGgYlu0RrxjvsAFxj8QR+EJedsnHiHoB2l5tOfIakLuqR3QpvkBDQNC1THrLbvy5hkfDQulAhnuJU0u2fF0e348uVP5b5IiR0m4LHreiJYU0fWVJScNLYpSbjQpD54dkY4PnruI/DQWfv586iouQNTeyZIAPKv2VSN4sKTyDu4Hzt//Q5b1XEmAST1a4/yHSvwjrBcS3H9+iLB19rmSnmiHyhHoUiyLZFhS5NNwRdkQDPvUQUUra+zI9UTiWmjSUIgaQcwjp4D2yNA03a2eD0Rn9aF4LmHUT/L/h7vsdDHMA7P3NtH0qqkSV22UkX8lXgrgG1+XNXBGz2n3IeSN16QjBBTT/5u9tuwUmdFMuHW/6B1AH0wUxtXg5PKda6F2BgYaxmS7/R5tDV2Vq5KIiNk3soRfKud4lzxSpvXHiZpyDTcaJmD93+Q3gTIzQLdJq86w+ArMSpsH95fsJHipHK1+NxCEzGa5ILfiebMx8p59AGCIEff8QwGJJCaa101TqnYyFNUhhpCIIkdGJ82UfIrOOxThtN7qJ3dxRMKzBm/P5WMF+KTbkQc/siNOPjU+2JOY63jbz981eX7GT2vxLiuzdQ+oVSpxYCrMCH/USwQ77V8fPve805wGHD9g5e6uOHQDfE9L8cURkgN5dhUvuh86XWiOKU/KmXzVYrbl9uPug1YMM8KYe9LGp2IzmRYvDGO132Kq7PQ7cDOOpICoD91Dugc+Fdz4LwJkphL9aNGoSg4CCVffwUD3ZLmGUKTEl8bpUpk7HjJu1E6ClNXegr5FdUwj78c9X37iUnTDvK8BOvjJ6DQKxjFBz9HTEQtvEPoa45QyXCBXtBnQW1pJfKKLDA3vx7m6GF/Gn1XNpuA0MIQfHDkcxijahBE/HMj/gmBEpOoTN5MFzt6smHtcuIfCmoxPeYajIocqk/qEncu6l9+sdfQDVterJrB/YX7Dj1ZOCSESOwnDQ5FjspxFBSgPCRYwGMzcimO1woyGoK0olXws9yz2kgHJ8RxMIY8naMrkGV5rmvIaHTo0RHdunVG24RIQZPjosUbnac8jcAWy/HuVz/boopOw9RJoxFftg7PfvwTpXnTkHe2gA5Au/Q0EiRtE/mjh6chyimcFb1vfA/MvD8IX836GFIuaxr7+l4+HaN70pdEZ4vAgBa47rmHsW3NSsz5YZNtRjkU3WEQLhs9CIlkHNmxzk6zXJCR3gGkjVBBWkAHdiI3ewuW/CxvmpjatEnon9oMR+vGoq9lJ37eJkkYdq79DjvXStVJGzQW3Tp3RMvoYFWY4ryidFV2UiqamY0oKTyILRu2YL6Wtz2mIb25N75nOy575XI2rsRO+mPXocdwdOkShcqaejISLEaJ82I4VtOm82d+jIJBTV3DalJMJ35ShVjcpo7tGow+Dz+AXr4hCBYCNDmzprySQ7uw96RRKNZaUZP2VuUeFfe+7Ez45/s42G2qr/dBUqf2CGPZnKkE21YsxJyfrO2RNOgaXDsy1eFWvqrScrUo+422d1Q73PTYzZhHt7LxONi64DVYTNMxuY/1K3rexk/wykKJ5yoi8vQYPgm9e3ZCUHk23n3xBzmpA24f0o7ee9bNk5VPNSgtrkJ0pCcK92xT62tPk7aMC8+vmfeCPWzqeTa0BsS2Bs1c8hyUhI7J9oJrW6wBLfri3mkkUP14ibxRpnR/DR2kEc222dh5e9gK7aVY/tXWwdu2Dt50Q949TyNpo5P5mHIm9RiF0cN6IzbYy67/uyE0tgOwk/tjILyczr9udMMta7wdawCGkmzc6fG6e6s1dmrnxc3d+qLyoBed0h/d/INAs4ngYyCpkyrx2uLdPP2hUuwA443WQ67DEx1ykfnrb8gvqaWs3ggMa4bWqR3QJjYMxzb+pqJj/PRf4wLQ95Z7YVmyEEs2WceXB30ZErR4RqP3qA7YuUQa42P7tdYIyrWIeuCuxwbg5LofaT6we5tF98C0qaPQXnM8W0OA7D3z96cjjgsnxi20Nf5vJr2ff3b9fkZ0B1w+6hJ0b9PE+TuebjHrQeuSoOhv8PESO55yVaP7YvqNI5EY/Gd8JPJAcCT1ur08Thrngn2c919nud08NeOBFozO+r2zfHqczgGdA/8+Drjt2bNH+zY5LzV0yzsKj6VL4LZtK6KCA0ltmkxvsm0EPlLGjo6IgTa51dVVKDhVAQt9Iau/ZBTZNomV0v/kX7fKQ/A4TNoDhZsQFe6FAH8fos8TbrRoYmeppy21qQ6VVUYUFNOn9MgeYCGUJSDhT6ZMQr+/+hDmHv8KP5dugle4H3z9/eFJ9HnI/Ksn/tURfTVVVWSYvBp9Q3tgSrPLybj2X0PfX8IEvZBz4kBtbS12VXnANyCYjkryQkUSFJGSkmrbSAiVKEz/bbSPOCyOw9GTTyPxqDXT0KC1qeo4LwuhlLzscSP16GoSGLQLMMPb27rQUDP9FR4SIpyqMIpjNPX1dGbf+bVBTinJXfYO3v1JWoxPuv85MpDJu+3GOBMKjx5BSbUHAv3qUUbPpnRRQJhqi+I0OEwVOFlYhIoK2kTwGCfpWlhEFKmM07z0j3Qm/LGa7AYVVqCi8Bh2uljMDpp6O4akJdgcHzQbK5B/eD92bFmPn2ShksoCWrhPumw8uiYGq1EwFSJzza8oMFWh5ATZFqJNqLOlc9/Lb8bQnnR1tZyTNWQK8w9hz65sLPzJUZDXYdAkjB/eFcGu5Emmo5gz4xWnAkQrcQ37xtJtb31s7GY0DC9Sqdx3qFzrlrEReWxAknD7zP+TbtapyMU7j72r4hp0zb0YmaocS6tA5oLPkVXoi+Bgbxzbtk3ma1/c/8KlaOKML3TD0JzH3lIFGndROYrdoqO/vCMLkqKR1rcLaYG1R2LzJuAhcmrXMjwhhLwSoaOmP4EBiXbH2oyH8M6Dr6u02lQpehSeuHdAA8fwbKD/fYGKP/DiYx9I7ZM2Fc9NTbMZUw1V2GyitQ5Nc7zk8RIf/RqCPos0no/LquBOth/NNTVw9w8kIW1j59WzKO8flsVM62DxjqX1sbMhRbrm2PjJg/hKyIFIwDrzOte3YtGXIVqtivc1f8B1jk/LICMyCfd8GfddhFuMV5MRFWQE3sPdjBqzF0KC/RqBi/R2zvr9qaXpAvTzu4K0KUuqSbzp50H7FvrQEBaBSI2W6OmoNlUU4sixEngESh9VPAKbIi46rFF8PR1uPV3ngM4BnQN/JwfOq0aSUhGLIQbm/9wEt8OHcSJ7B9xzc0hj5iQZ/5C/avK11lFNYO5KAho6VmCJj5ey2n5qUdCd96fFPx7mNvcQPftxsnQb3E+RIdYq2n6YZCVg0lpy86fb6ILboS4uDQhK/Evpa+Ebj4ea342JVfuxuXwbsst2I6/mOMrq6GgeuRDPYDT3jUZKYDt0b5WGJH+JPv2rgNRM+q/Egci6chwppltH6OwZm912I+mPJBCiL0iyEEjqMyLVqq0kDCZRHEmK6miD4Ua6Sm4ijv2ckfAIoRL56clJZGKb9iNmxHkaqXDHG7j+sjZx80ZQkFWI1dgxYS7ZhUV0k5ColmEU2pzRUTJPRMS0QIRcyWbys7FlwzMAUc3oz45Jjc5vl+/vD3oiOKAIG7+TvoJr6eHjWmndu6FL+yQEs5yMv7BrANy8AxCTlCL+Bo8rwb6dZDtr/g+SACE/G3nlo5CufU94uuPk99+Ddc0cHN0QNrxvT6R3aCUJ9TRluXn6Iiq+tfjrPWw0Th7aQxfwkfBKPnOVvTIPo4aR4WAtcdoCSLe/TMbXZdRUpJGBWt4QntaRLaKP5ywRYCbO77IAF5gsXmjZvj3x7uyEjEZjqFXTIyAJ19w5EfNXlGLg+P5IsNEQsaD8EJlwzrejo0ssQp1qihAcadlNefRmGJ9YhA53XYkYjeHVJp0uxz1tvBEZHkwCCwWnpF3hRhprCh8GTXsA/clgsBJWIOHdFP0vSUPuEs35R5FowOWXpTVwlEvF8C/1mPHH2lXIl/vRcBKWOr+0wHn13fgsM/Vx8TjTvugcpW0sz8eh8nzMF7GQc2hb2xwXTchUuA0znp0j1bfLJMyc0tXBzkw1HTtdQAb3hTMkIFwzphwYRe9r0kuUHM8tit/lU5p/pGbX+EnbJEB+h4oWawSuc3t/uiTwwkigd0VEs3j1/a4S1Qi+KLCeARFoQTfASk5eIZxBfgWP/tQ5oHNA58CFxgG333///fTvmwuNap0enQM6BxrkAGutsVaS0WgUC/c/e/HOQif+86ENLmsjKdpzDRL5tycacXjXHhRV/T97dwMkR3nf+/4/7++z7xLIXiIb+YCFAYNi8EEOdpRwwA4hxZXLl+RA3VjOtS9U7PJLRafwxTlch1uqQ10cynbJcY6N7Qs3l8TRoULFZbmSo5P4INvAFYlx0AnHOEdGRkha7a52d3Z23uf+n+6emd7Zmd2Z3hXa3f52Stqe7n66+/n0Otr98X+enpRn//w7zWqHX/tf/1Def4Wr6uWi3+cGvIHypLz0T69JWP+jQSqblaxWVZrvjVgrReijU2V5/ZV/lH/8HzF53y067Kqt5eyrL8qLrxW1ciarIWJWUqm4JPV6aZ0AtN+lOPu6vPjsP0r47e+T65arFqrNykvHXpLZSljedPW75LK2Apru183Jiz/8icxr6nTJVb8sb7HGmHU/+mLumX71ZTl53gyhC2kRcVVCyWF56443L/Ffi3ucfOVFmc6+TXZsaX+6i89e02oJU0TTWCL6PdXMpRobffC1OP2qvHxiUiZ/9qx8pzmk6Vb5w//rltYcXD5w2NBdLJ+VP7v/4WZV46Xv/g25/YYr5VKdw7NaPC8nXvyR/D/faVVLXv9v98vvXLdlDbusFUnf/N/lz52KpE/+n7/brCDs7SL8+9mbE0chgAACm1cgcPz4cYKkzft86ZlPBUxwVNNSd/PnQodIDWITJAVNSb2Z10vX1/1SfFX+5IEvyc/cN3rVh+Tzv/uuC/LLsvsyrCOAAAJeBV575k/k0b9a9P+5ZO8nPy/vftPyQZzX69HuwgjkXn1G/o8v/dXKJ3+3/ru0d63/XSrKM3/ygNjfRpfLxx/63+SyfrJ3/v1c+blxBAIIILDJBZqVsJu8n3QPAV8JmCDHVBJsjMqgi/RoghEZu/RSiQ8nZWoqLlf/ynvlPe96CyHSRXocXBYBBHoTCMbG5NLLtfIuPyXxX7pa3vO+98iOEUKk3vTWz1Hpy94jD332Mvnhfz0i3/mvLy29scuvlw/9+q/Jrh06ofPSvavcEpO333qPxM7pcPTwqIz1EyKZK/Pv5yr9aY4AAghsfIHASy+9REXSxn+O9AABBBBAAAEEEEBgAwqYYZu5XGticn3Lir65sd90ZwN2nFtGAAEEENiwAlQkbdhHx40jgAACCCCAAAIIbHSBYCQm2SEnOEpQXbbRnyf3jwACCPhBYO2rZf2gRh8RQAABBBBAAAEEEEAAAQQQQAABHwrom2IZ2ebD506XEUAAAQQQQAABBBBAAAEEEEAAgb4FqEjqm4wGCCCAAAIIIIAAAggggAACCCCAgD8FqEjy53On1wgggAACCCCAAAIIIIAAAggggEDfAlQk9U1GAwQQQAABBBBAAAEEEEAAAQQQQMCfAuFvf/vb/uw5vUYAAQQQQAABBBBAAAEEEEAAAQQQ6EuAiqS+uDgYAQQQQAABBBBAAAEEEEAAAQQQ8K9AQN/axmvb/Pv86TkCCCCAAAIIIIAAAggggAACCGxAgaPPPCPXvvOdb/idh9/wK3JBBBBAAAEEEEAAAQQQQAABBBBAAIFVCdRqNalUKqs6h5fGBEle1GiDAAIIIIAAAggggAACCCCAAAIIXEQBM8DsYgwyI0i6iA+dSyOAAAIIIIAAAggggAACCCCAAAJeBExFkvnzRi89B0mBQOCNvjeuhwACCCCAAAIIIIAAAggggAACCPhKoNcqIxMi9XrsWgL2HCSt5UU5FwIIIIAAAggggAACCCCAAAIIIICAdwGCJO92tEQAAQQQQAABBBBAAAEEEEAAAQR8JbBSkHTjjTeK19FlptLp2Wef7ehJRVJHFjYigAACCCCAAAIIIIAAAggggAAC61fABEnmT7clEolIMBjsO0wyIdJy5yZI6ibOdgQQQAABBBBAAAEEEEAAAQQQQGCdClRXmCMpFotJOByWUCjUVw+q1apUKpWu8y8RJPXFycEIIIAAAggggAACCCCAAAIIIIDAxReoaeCzXEWSCZGi0agVJvVztyZEMku3cxMk9aPJsQgggAACCCCAAAIIIIAAAggggMA6EDBBz3JvbQtrJVJEw6Rbb7utr7v93uHDYkKqbucmSOqLk4MRQAABBBBAAAEEEEAAAQQQQACBiy9ghqB1qxpy353XCbe7nZsgya3LOgIIIIAAAggggAACCCCAAAIIILABBEzQ0y3scd8+QZJbg3UEEEAAAQQQQAABBBBAAAEEEEDAhwKmIqnb8DOLIxAQfWWb9ea2vnicdt3OTUVSX5ocjAACCCCAAAIIIIAAAggggAACCFx8gV4rkg7rnEdelm7VTgRJXjRpgwACCCCAAAIIIIAAAggggAACCFxEgV7mSNKaJDF/+lkaxxMk9aPGsQgggAACCCCAAAIIIIAAAggggMA6FjBBT7fhZ+7bvuO3fsv9ccX1p/7Tf7KO6XZuKpJWJOQABBBAAAEEEEAAAQQQQAABBBBAYH0J9FKR5HmOJO0qFUnr63lzNwgggAACCCCAAAIIIIAAAggggIBnARP0dAt73CcNBoPujz2vdzs3FUk9E3IgAggggAACCCCAAAIIIIAAAgggsD4EVnprW2OuIy9BkmnL0Lb18Zy5CwQQQAABBBBAAAEEEEAAAQQQQGDVAr1WJB36y7/0dC0qkjyx0QgBBBBAAAEEEEAAAQQQQAABBBBYfwK9Bkle75wgyasc7RBAAAEEEEAAAQQQQAABBBBAAIF1JrDS0LY//Y//cVV3zNC2VfHRGAEEEEAAAQQQQAABBBBAAAEEEFg/AqZiqFvYcyHvksm2L6Qu50YAAQQQQAABBBBAAAEEEEAAAQQugMCFHtrW7ZYJkrrJsB0BBBBAAAEEEEAAAQQQQAABBBBYpwJUJK3TB8NtIYAAAggggAACCCCAAAIIIIAAAutNgCBpvT0R7gcBBBBAAAEEEEAAAQQQQAABBBBYpwIESev0wXBbCCCAAAIIIIAAAggggAACCCCAwHoTMBNtM9n2ensq3A8CCCCAAAIIIIAAAggggAACCCCwDgVMRdJ//tu/fcPvLKDpVb2XqwYCgV4O4xgEEEAAAQQQQAABBBBAAAEEEEAAAY8CPcY0cvr0aTFZzeDgoPXVXM5LdtPeptv15+bmrB4FPfaLZggggAACCCCAAAIIIIAAAggggAACPhMgSPLZA6e7CCCAAAIIIIAAAggggAACCCCAgFcBgiSvcrRDAAEEEEAAAQQQQAABBBBAAAEEfCZAkOSzB053EUAAAQQQQAABBBBAAAEEEEAAAa8CYa8NaYcAAggggMBmEwjvGZDM7rBUn8/J7OHiGncvKcOPpqX+Yl7mHstJpe3sib0DEr8uKrXnZmXm6TW+9s6kpN8dETlVlNzhgn3lXSnJXKt9/XFe8sfKbXezmo8RyX5uUMJSk/LRWZk7spbnXuG+xlMy9JGEBKQuxb+YlPnjKxzf0+6IZD6dlYg+sfkvzIj7ycT2pCU0U3T8tN/3D0p0qC4L3zxnXTu8OyupK+pS+Js5KZ7s6WKrOCgisT1RCUerMt94xn2cLXbXkKQuq0n+/52RQod7Dd82KNlbYhJSh9nPTC5y6OMyHIoAAggggAACm0CAIGkTPES6gAACCCCwNgKh7VGJXqGBS6a65kFSeG9SIqP6z+6ejCR/rEHVMfc9xyT+b1ISTem2dHLNg6TYu1OS2mOCpEAzSMp8MCvJ8YBUwqU1DZLCd2QlYQx1qT//BoZI5oJbwhLZEbWCpErCbFiLJSTRq2MSjsQkva8oxcfsIC59/1ZJXRUSKZel+uEJDVZCEv5XEQlF6hJyrp36n9IS3yoSe2tQzj4ws6qbCY9HJLglJMGBkISG9M9W/TOinwf1TzoowVTj7bp6/YnX276/Vrp0RJI3JySSFRkYFSnsX3qvFQlKRK8p2s/EbSLFwyudk/0IIIAAAgggsFkFCJI265OlXwgggAACfQtU83WrTa1U67vtSg2SN9rhisyWOvySX5Tp7xRl64diIqMaWIyL5DpUhax0ja77nTynXrX7Zx1nrWv40F4a1fUkvexIysCd2gdnCd+2RUZ/Q69RbWxZ/DUQ0uqdr5yV3JpUDum5z2p4pV9MRVJd19dqqZv7tx5fI6zR53NgXuLfympwFNGAKaEBk165cdyCHn/bsBUimTsqfndpMNPt3jIPXiLJt2gPrG/BgATMJAQmv+lxqZc1SNoeFznmVJ710k6r00yIZJbyC6ZdRMMiDeRK6qifAlGR2lxJKuW4Bmoi0fcOSWKuKAF91HXVDkTrUjmcl5I5AQsCCCCAAAIIbHoBgqRN/4jpIAIIIICAW8Aavnaj/vPXHm5U9FfmMfufxeD2hAx9QtfjreCgeY6CDnH64kx/vzSPZyW2zT5XbSYgwwe2SGBROGB+XW9sCErqwFaJnqzpMa7r6+7KD2Zk5pB7cFXzrtbBSkKGD+qQNicvMzcUNBVYyy51CfZROZT6xKgkfikoVrDT6bwhrcyxtgcl/sktoqO8Oi7GtfZaXqa+MNdx/+KNehLnPPWyHTCmNexJ7WhNMxneMyRb36utrEcYkMRnt0mrWwGJf3SbRG+ek4kDK1+v9GpVkjsii78/NBySSOt7oT5fldpMXWr5mtSmdD1XkdKPNeg5VvSUC6Y/ENc4SBetrso/qd9fuwYlc3fS3rYYw/oUGE9I9t5WD83G+nsiq6666nApNiGAAAIIIIDAOhRY6Se8dXjL3BICCCCAAALeBazha1e50o6Op9LhTDc0gp22A6pBMQUn/Syp3044AUdVSic1WLhppX9+QxIZ73D9gVZ40fn6EYnfEZPAa0VZ8DjvUey2jNajlJtD4Dpfp23rzrSM6DxCYS2EsZeaFP56XvMXrVZpBCAahtTzWs1yW7pZ/SLzZSktGuLXaN/5a+jNUR3S1Xlf+9bg1rBj3r7H/hyqdvBddGhMMp9Ia590SJcZcqhL6OqMDH5CA5ak/dn62x1WudfNzpoTAOmlAtmVnp19zuJjEzL5N6aqyw6tKifLEt83KgM6/5FZ6qcW5Oz+aWvd/JXev0VSN8QlellBcp6CJB1+d4V9b9Xn58XUI4XjGlLNa8hniLRyLZBq3bupeBKtVBLdFijYFUuiwVx9TSvbTM9YEEAAAQQQQGC9Cqz0k+x6vW/uCwEEEEAAAW8CjWl75itS/oX+st7IE0wIMKhz7GzVX5qX26dN9NfoPpaUJMxcOrrUTxRk5u/0QmH9rdv8Em6qoNyL9a+yqQ3R7e5fzMP6S3tYw5nHlo+wYvsGNXDQkGy2KOVjk4tO4b7McutxDZLiOk9ObNu0TK5wPXOe5L4RSe/R8GrRSbUi6FcTMv9/n5W5o40dSRl6RCetdoZQmSF+M/eds4KLxhErfS29kJfgL8zAtS5LPCyxa7SaR3fXXl6QUrcRZepcfXa+y0mczbsSkryhNUzPbA1ui6qLtj1nhzzlI9Myf0pDv+b8RG2nLGu10CU6qfnNKwWXi9tVTraqztKf1qDoevvHtfpJDZHub4RIMRl8eLhZ6RYaaYU9i8+2/KekBmPWd2dVK+0O5iX20TEZ1PutnViQqQem9XsoLsNfG5aIhoS1E/MyoXM9JT46KtmbdWL4X+Rl4sFuyMtfl70IIIAAAgggsHEFCJI27rPjzhFAAAEEViFQnyjK1B8t/iU4fNeojNweleX29XvJ5Kf1zV7Wb+rOXDk6H9CMmRNIhwel36fz0JhKnUUnXfzJVPRUfzQt+R7mESq+XJa6BkmBrE6evEvzpD6qfexb0GqhIXutesqdZC26wdYHHbKXaoZIdSl9b1ZKGX1D3E0anKTCkrp3m8T3FKQ8H5LY9XbAYxrXTxXk/P6p/oYHarvCk+eXD57GB2TMCpI0dPvmtMydbN1q32tndbjYibIElCH0Fp3oWp9h7ZxOrD2pIVJGJ9U2J0zrEK+748tUPqnJcz04Lrk5U1mWlOQtOkH7kB3R1c/oG/f+piipu7ISTGrF3I0JCTuVUrVXTKBzfslZVt6Q1rDM/lGw8vczWmmXkJHdduhl5q8yd57aP2CFSKZCauGrzv9eNNg0S3BHSob3lWSqh8DRasBfCCCAAAIIILApBAiSNsVjpBMIIIAAAutSYOeAVpNYkYN1e3XX/MexWzIawvT2z3A1oW9WO758NZJ1gaMLUv63SYnqMKrobk0Zjq1QddOGFr5NX+9ublerU0qHG6VbbQe5P56cldm/1tfe36zD/b4+oUOrzM55mX9WA51PpayAJXRFvFn0ZfaWv3deph7XMW4XdFlcH+XpUidzMv1ATptGtCJnzAqSqsdm9d6Lkv7cJbq1JtWzOrG1OblWHhV/WJRaWIfy6ce6VptFbkraQ8QKOo+RqXbrp8s6+fXAh9zj53Ro3FYdardvcYWUdenvq+ef9nNy08osERk4oCGn/UHqozo88UtRCVsbapI/qMHUnmFJXWMfUdHrNCaAXzg4K/F3DOv3mZ5lz4BkfrQgcz0Enc6l+IIAAggggAACG1ygt59gN3gnuX0EEEAAAQTaBQJvSsrYl5qT+li7A/padbMst08n/ulxicnQvXaYYjdYXGlU1+qWyimtRlrhVVemMqT4Ug8hknWRohT+uaLzO4UldKW+YUtDnX7qYeLX2T8WmHvrNZooPjkpE0/aPTThVVyHhEXf1n1+opCGJENa0VN4Nu9tHqedWRn5/aQE3W+gazyR5mTbOuH1/Vsl7n5WZuLyhbLMfmZSWgPHGg2X+9qIWvT7wpnvKfdHp8VETKJB4djt+jUSkmDaREh2gBXQQKm+oM9bJ2uv6xDDiT81B/exHCtpFZdWHOmzN+epL2gYpd8n4e2tqi5TIVR4YlJm5pIy+rVLpPrdaZnuZyJ2fQ6x8dZwuMg1rZCq9mJOchnt2z5nEm699cBbsjKmk6kHEjrMctFIvaAkPzosc5+c6qODHIoAAggggAACG1mAIGkjPz3uHQEEEEDAu4CGAkEnOFpykuX2tXKFJc3cG9KfG5KoM0zMvb2xXno6L/mS/jNsJi5eZglE9Q1jjWlxljmusWvhaEkyGiSZ4W2JndJHpYhO2Hy53bnqy67SqcaJ27/u1CFsu3SI16URCW8NSWg0tPhNY+Z4fctZ+ScFqVR07qLrohLUACI4GpGo+XNTSrK6vzpRlerpivW1fGxBFo6vUAmlLz4LasVVKwJpvzH7cyCr99O+y4Qg7dv6+RzXcPARDVNemrGHcyXsCiRzisj1i99iZp9WA6C+hxealvpGuY+5orydGRm+N928dzPMbU4DMRMvZh7SOY40sArdOSTZE6d7H85oJuae16opU1n1ekWHqenzMZfWarTcwzmJ7tMqLPPZWULji39krGullQlBzbOQ0biM7Ev0NKdW43x8RQABBBBAAIGNK7D4p4KN2w/uHAEEEEAAgf4EpnXOmac0MLEqSbSpBjrBnSlJ6sTG1nw03ylIoMO+Xi9SOaNz6ejbsCo/mJf6O1I6yfTiCCO2LytZMzF2D0vl+1p90svQNnOuYxrcFHRuHfO2sV1acXW8h1DItNutFTBWgVZNSs+sXAEV3pGQ1K2tKhZzCmsx4dBrZSk+o0HIdRkNs/SkPzgvEx8uSGLvgCR+WSultjmhUyQooW3mj+0QLuvb5lYaIqXD6Wa+olVXVyUlpRM+m6V8ZE4K5/T5ORVD1kbrL/P2sbCkG6+yN5U9rZ09rrXKmsIafpml8pIT/unQwdnHSlbAYwK/QFSDqqSGXCboGtQqJX3m0QNbZEQnP588sHg+rp4urvNoDXwkK/EdTnpZ1Wfzt7My7RoaOPfAeYl8bch63onfH5PyhyesgGnl8+tk2h9znvOuQR2K6Fjq+a2tP9K5rd6mZ8lrRVRRwz6dYLx2riKVSR3OpxN7117JWc9q4GGdB2ubHpfuMWFd+cY4AgEEEEAAAQTWuQBB0jp/QNweAggggMCFEajP6FuqjiyeQyg6phMcm8stVCTfbV+Pt1N4piDJrQGZOljQOXacWZFdbesz+ov5vMYaK1QkialIml2+asl1Wl0tSEnfRhfZEZSwCXGWn5662TS1Wyf+Np9myyuHOXpY5elZKbxnRKIJM1dQVSonSlLSEKvgqihKv2dAAhpoBZ2QYeGQTuisf8xihsHFrtI5ebbpMLwtGiwtlCT3ZG+DzkpH81I6WpXorhF9Y5oO+XprUPKPdQ5q0jrEzY7w6lL4y8m+JviO3TYgyffGncmmrduW+rQOOwunZcsTw5pg2c8lENQrLJujhCS7c0ZmVwrJrEvoRNu3xbWCK6E+WllmX1b/1hDp2QWpZrQq6v6kVpxpRZ2psDIBlhUA6iGRiGQODMrC/f1MvJ3QaicdKqjN62c0XHpcg0cNsJKX6CTa989JeK9OQH9nwn5j22P6vxcdzrdFh2wGROdXuv+0zHxJ3+z2tpr+b6m3Z9fsDisIIIAAAgggsGEFCJI27KPjxhFAAAEEViWgv4C3L2bolbUst6+9UbfPx3ViZis4aPyWv/jA0iGdO+fQ4m1r9an0k4qkdKhS4BINJfSky1fhmDAkIfG3O8Pa/mmhx3mVyjKz/7SGHhooaDGLmT8ofI2+iU7/WEtZq3EGbOPgpVFJ7dWwqOFrHaCVLpMVnf8pJ/mjKwxns8/Y9ndR5p8py+CtOm/Q9s5vD4vdMyqpq+x+1fQ6M0faTrHMx9g9Y9a53YdUX5yTcw/r2DoNUSJmWJ+Zw0gLlvRvDQTVYMgeOlc7pfNfTWtQqP0zw/Yqr1U0YHOfaZl1HTKYvdsENe2LTqDuVES173F/DownZfiegh0IuXd0Wc8+1HorW/H5qgwd2CrRcTU7pUMvNRwKOnN+B1xBmfmOMYP6glt0RYcjzp/scnI2I4AAAggggMCmFCBI2pSPlU4hgAACCKwkENiWlK1PLH4zVqPNcvvMMUt/yW+07PVrXKuUhhdVuizbcr4k5z92rudJokvPlaR2p5mTSOcm2mXeo9Z9qZuqmr1pCVshjw6h+7t894OX7NHhbXdndZ6d5ZeAVrikx7sdk5LQ9Ok+5nJqnaf4+IQUrr5Uh1YF9O1hQzKsw7CmnrSH8kX3jsjArfZwLdGhZTMHTADU+1KdsOO32rmqBMz8T9q0ppOQ24sO6/ovOi9UMxjTKCkflPjtOl9RpC6Vfy5K2WLUuGU0LGENlHpedChi+VxCImkNqLRarZ7TuYjM6DqdXLymDzJ8pT5XDXWqL+tk5c+XdW4pDarMfEfmLWyPjEpcq+Aitw5I+u8Kzbesdbt29sGtktjemAnJ3L9e1FlqammW6JtdCZKzjy8IIIAAAggg4G+BlX7287cOvUcAAQQQQKBdYEaHcbVvu9CfzfClfq5xUoOF2bS+nl2rhK7VeYycUKDjKcphGfo1O3Cpn9LwodfKGetkNR3SVtE3eS0+s5mEOdycnLku1VOahLTnEQkd0qavj1/tMrN/WkIHNZQzr6K/fViGh+ekWIlJ2pk/yZo8+kB/Q9rMPVUOz8r5iaAUj9Vk+Ktj1hC61lOIS+L2VJcATSux9qi9u2N7olI+OtFjEFiU6U+edlprOPTQoMgz52XmsAmxYhpAjlhBUu1EQeYPFyRz/xbJ3FOT/CPnZObReZ2TSSflPqPh0opVQjpX1S+1PRSd36ryc53f6kfzktNzmyWoVVZmqc8uX9dmHcRfCCCAAAIIIOALAYIkXzxmOokAAggg0BRwqkjqJ/NyVueSCY87G06WJaxvqhrQCbCX7Gs21oDBHGdeca9DejQvWeWi8/Y8pgHATzV4WXImHR512aDOR6Pz4VjDp5YcsMwGrYg5WdH5h+pSflmHJ711mcQmoiHE4bwE3hvV8GS52qVOl9NKnwfOdtiRkdFvZXTolwYQJ/Jy7oFO8xfp6+SfSEuwUJVSX+FV++V0GNd90zL06KC+DU6rcW7KaG2Os8yXJffQhMehVxqoWG9c08GBbXmLmXdq5itzkjTD5ip25Y5O9S2xX0lYFUnl5/JSyTVuQiPAsg7ha3zs+Wtc+6Rv/tM+yfZRqb36ulZtteLEgBlOd9uI3oP9nZM+cImEn9DhknebqbIblVPLXUzn0vpJWYKXaBd0fquyVlHNH7HDI9mVktSemH7WCdvH7GtWf9H9nOnPbZXkFv0+evK85DwNU1zuPtmHAAIIIIAAAutNYOnPrevtDrkfBBBAAAEE1lCgeqqiv4JrWFS0AwATDDWWUGNVhxGZxb2vcYzWvUjmd/UX/E8NSemvp2TaGUrV2t/fWn1GL3pSr9WpWcaafae/aiTnPLkDZ6WRZcTe2unk9jYzb1Hx6fP6p/sxfe3ZNSCjn9Dhalb4okPlnuoUIumQqb3O6+b1uFY80teVXAdr1dOkVszoEDT3Untdwzj3hlWvm+8LDXgeHpCwVmFZQ86a5ww4Q93069viEjTD0ZpLTMb+dVIWvqzPpKfQTOc5OjhoVVmZU1RfzC0d+qdvvJPDk3I+NSzZO/V6+n/xu8f0DXjTMvVY4xu5eQMdV3JfmGh+j5gD4nfo5OLv0WF1+iY9OTkvxbQOy7NSOa1U0onUuy2BmM4NNRSyJgcnSOqmxHYEEEAAAQQ2jwBB0uZ5lvQEAQQQQKAHgcrhaTmrf5ZbAgOLA4nFx+ovzc5QrtUHIBqiDOhv6uOdK5LEmbx58fX7/1Rv5AqucMOOyjQM0eFMa7PEJPPprCSu18mvnROWv3deZq2qng5XSDpHaWjXuJcORy27KbwrLanfTEpsh+vtZjrnU13vwARkwR1JGTyQlKoOAyvoW/hyq36zmN7pTq1G27bc94cZDtZpv9YstQ0B7NS58G2DMvTb+hY15xQmRLIn+O50tIaAh6Zk4r9lZfQPdNJz7bOZK2pE35I3+cVGjNi5ndlq3uoXuyYu0XdEJaJDEd0TateKYRm8U4dFmkXf5JfvFICZ4idrMU9Qn+fapnbOufmCAAIIIIAAAutNgCBpvT0R7gcBBBBA4OIJWNUXevkhfcX6PWlZeK3WDEUaNxXScCJsvYhNJ1X+afcqjcbxy38NSFyH01mnW/7AHvZqpcwjWsXivCmt2UCDmkDKnufGvNFry1eTVnATTNlBTmTPsIy9t0NVUKimFTRnZK5bEORcIGyGQf0bDXOuagVIGk9JSUOkafMq+cZxOoSwUeEVu2NQMjqE0Cz10zqEzDmmly/hXUlJ3JiQmIYfIZ0DqrXoML4X8pL7wowOOVwcaoW2xyW1T//8TlXK/70kpf9vQUOl1r21ztG2Nh6TcMYdsKjj8fNy/o9dbRe0GmpOK3Z0VqSRz2ulkk62XXhiWhYmlppWl7WMSPZzw5K4ohVCVZ6bbQuE3P113au+IfDch6s6l5O+gS2lAdENGix9WuTcF5YLk+KS/QOdW6rxPe+crnaqKAv6Nryovo3P3qf9+cvJjhlR6O36HC6JSuxN9vdXfbm5uFy3yyoCCCCAAAIIbGwBgqSN/fy4ewQQQACBNRQovqzD3jTgMJMqR2/NLp4wecl1arJ8MLCkwYXdsEsrS7bav9AvvpArfAhpzzRocG3REhZ9jXtbmGC3t99Utvhcrk/jOg/Sv9d5kNpSMFP9k/+zqUUVLJkHL5HkDufeTFVUKyuRyj+4QhnX6Zes7tQ5lf6dzqnkamsdoxVV5X/QV9B/ccYVSBVlTodtzWmglL4vLfFrNXQywVk8JJFrdOiW/knt07DrKR2aeKhLjKX9GzuQ0QFjjUWDw5ftEpyia5hXYt+oZG/UH6eqLcf6hM4/tGxo1Din+2tQQpc2ypB0SOChaZl52tybhpqPDttzJbnsloY28zL1sbqGSRom6jMOabVRVAeu9TKPl3kbXfl4UQrfnZXCyZSMPjHQfERVDbNmjrju87i+EbCa0ueg/xu5U4d4NndpkPdij8+y2YYVBBBAAAEEENiIAgRJG/Gpcc8IIIAAAhdG4Oi0zI4HJfOrUQmY35BdQ8GaFzS/65dqUnpmTnqdmtoMs1oc3+jwK+fcha+ckpmjzbMvXtmlk21/Sifb1vaLwp/FR9mfdPLv3FP6qvpO+zxt02BguTDkpIYlE2lJjOsVq1qRo5M1L/zV9KIAqXHZ6pQZPudEMk5WUp+vSvG/aEjRLchpNG581aDDehPdkG7QKqvqqZIUf5iXuaeb46saR7q+FiV3UP/oluiejFYyabhyeUSCJvzSibjzy11b586q6gO2jtV7NpU6hQ7PKaDDyIJOxZd14Wm9r+XcXHe3eFXf1vblORn6rbAsHJjW6bwbS1GDG+epOnbW5N1HW0c0jtTX82mYFJDhhxNS+ua5FUKkgsx+eUqCZ/U5nmydQWGk8JIOGdSJxKsvzsm5L7Z/l2to97cJSf9r/d9Is5k+j3/sMI9Tcz8rCCCAAAIIILCZBAJ1XXrpUCDQ+nGhl+M5BgEEEEAAAQRsgbgOEwpFNZh5er71y/24vkL+qqBU9Y1p3atGIhK3KqR0mFkvQ7HecPCYJG4LSvnwQsehT51ux35Lnk6CvSi86HRkh23jCX1LmcYler3VLNHdGs69mpeil3tYcuGIRHdpwqO3FEjUNUTqUuG0pF3vG8xcRiHX/Eruiqjez9LfkbFdEe1LY3Kt/tpyNAIIIIAAAgisTqDHmEZOnz4tJqsZHBy0vpqreslu2tt0u/7c3JzVMYKk1T1fWiOAAAIIIIAAAggggAACCCCAAAJrJtAtyGm/wMUKklpD/9vviM8IIIAAAggggAACCCCAAAIIIIAAAgi4BAiSXBisIoAAAggggAACCCCAAAIIIIAAAgh0FyBI6m7DHgQQQAABBBBAAAEEEEAAAQQQQAABlwBBkguDVQQQQAABBBBAAAEEEEAAAQQQQACB7gIESd1t2IMAAggggAACCCCAAAIIIIAAAggg4BIgSHJhsIoAAggggAACCCCAAAIIIIAAAggg0F2AIKm7DXsQQAABBBBAAAEEEEAAAQQQQAABBFwCBEkuDFYRQAABBBBAAAEEEEAAAQQQQAABBLoLECR1t2EPAggggAACCCCAAAIIIIAAAggggIBLgCDJhcEqAggggAACCCCAAAIIIIAAAggggEB3AYKk7jbsQQABBBBAAAEEEEAAAQQQQAABBBBwCRAkuTBYRQABBBBAAAEEEEAAAQQQQAABBBDoLkCQ1N2GPQgggAACCCCAAAIIIIAAAggggAACLgGCJBcGqwgggAACCCCAAAIIIIAAAggggAAC3QUIkrrbsAcBBBBAAAEEEEAAAQQQQAABBBBAwCVAkOTCYBUBBBBAAAEEEEAAAQQQQAABBBBAoLsAQVJ3G/YggAACCCCAAAIIIIAAAggggAACCLgECJJcGKwigAACCCCAAAIIIIAAAggggAACCHQXIEjqbsMeBBBAAAEEEEAAAQQQQAABBBBAAAGXAEGSC4NVBBBAAAEEEEAAAQQQQAABBBBAAIHuAgRJ3W3YgwACCCCAAAIIIIAAAggggAACCCDgEiBIcmGwigACCCCAAAIIIIAAAggggAACCCDQXYAgqbsNexBAAAEEEEAAAQQQQAABBBBAAAEEXH1oMCkAAEAASURBVAIESS4MVhFAAAEEEEAAAQQQQAABBBBAAAEEugsQJHW3YQ8CCCCAAAIIIIAAAggggAACCCCAgEuAIMmFwSoCCCCAAAIIIIAAAggggAACCCCAQHcBgqTuNuxBAAEEEEAAAQQQQAABBBBAAAEEEHAJECS5MFhFAAEEEEAAAQQQQAABBBBAAAEEEOguQJDU3YY9CCCAAAIIIIAAAggggAACCCCAAAIuAYIkFwarCCCAAAIIIIAAAggggAACCCCAAALdBQiSutuwBwEEEEAAAQQQQAABBBBAAAEEEEDAJUCQ5MJgFQEEEEAAAQQQQAABBBBAAAEEEECguwBBUncb9iCAAAIIIIAAAggggAACCCCAAAIIuAQIklwYrCKAAAIIIIAAAggggAACCCCAAAIIdBcgSOpuwx4EEEAAAQQQQAABBBBAAAEEEEAAAZdA2LXOKgIIIIAAAggg0LNAdGdMj61J6Xh52TbWcXN63Mnux4X1XOa/btWOF6Wy7NlWuzMu6Y8mJFSpST2tV3ytKLOH8qs9Ke0RQAABBBBAAAHfCBAk+eZR01EEEEAAAQTWTiC2b0wG90SsExafmJDzhzuHROn7L5HUVSYiqkru/jMyf7L9HhIyeGBAYuN2kXT9xLycfWCm/aA1+Ry9Y0gGPpSwAqvWCRMS/5WkzH7ynBRaG1lDAAEEEEAAAQQQ6CJg/9TWZSebEUAAAQQQQACBTgLFl8tSd3ZErot3OkS3xSV6ufOjxnRFim0hUuKeERl7YqgZIlknCXU51Wo37xmSQVeIVC9oRZJzzsBoVAYOjoqpr2JBAAEEEEAAAQQQWF6AIGl5H/YigAACCCCAQCeBowtSmbd3BC+PdQ5hdscl7GRMlf+mxzfOM56UoUculeyt9nC2xuYL9zUmQx9MSMC5QPn7M3L2907L2bunpHCqZm/NRnXIW/LC3QJnRgABBBBAAAEENokAQdImeZB0AwEEEEAAgTdWoCiFl6v2JeMRie1aevXErqgT3tSk/GxrHqLYLSmJbnVinWpNCk/NSdkJpZaeZQ227EpIJGufp/bKvEz9aeNiBZnZPysVZ1Re+J0JYcz/GnhzCgQQQAABBBDY1AIESZv68dI5BBBAAAEELpxA/tmSMzwsINHd7dU8EYm/zYllZsuSP7b0PmonFuT8/3JaZg5pknOhhrTpZePNQKsupb9pn38pKEF7qieRbEQSO5feJ1sQQAABBBBAAAEEWgL8h7eWBWsIIIAAAggg0I+AGd72Ya320eFrobfpMDbJt4av7UxKeMg+WfWfC63tuqn6ckHyMzmZO7TQz9U8Hxu9zEmpyhUpHXWdZveAjH005Zp8OyCBhGs/qwgggAACCCCAAAJLBKhIWkLCBgQQQAABBBDoTaAgxZ85w9uGohIfb7WKvTvqBDRaBXS0MZTM3l85OveGhUjmivXmtNpm3V5id43Ilns1RFpUCRWQ0Jsa5UnOgXxBAAEEEEAAAQQQWCRAkLSIgw8IIIAAAggg0I/AvA5vs5egRN/Xentb/Eqn6Hm+LIUOw9r6ucaaHZvT+Zj0ZOlPb5HB22PN+ZsK31sQZ8ptCY5SrL1m3pwIAQQQQAABBDalAEHSpnysdAoBBBBAAIE3SOBIQcomndElfHUjSEpLZJs9mXbtZwVpRE32URfx72hQhh7cKqnrnbBIh7rN/7HO0fR3teYb3S7i3XFpBBBAAAEEEEBgQwgQJG2Ix8RNIoAAAgggsF4FFqT0c7ueJ7A1KmbK7fDemDN3thnWlls/N56KSHSHM5ZtuiQzHz4rOVMtlWkMeatL9VRl/dwvd4IAAggggAACCKxDAYKkdfhQuCUEEEAAAQQ2kkDueX3rmllCYYnt0be17XTmGSrosDb35Nb2URfhb7s6qnFh87a4yY+fs4a5mW3RayLNCbdrE05fGgfzFQEEEEAAAQQQQGCRAEHSIg4+IIAAAggggEDfAof17W1O/hLenZbom+0fL2o/K0qx75P13iC8OyMD+0dk8L4BiS7TrPwvrSqj2ks5mXhgetFb5KJva8yLpNNyvzEvklvmbtmFAAI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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 资费管理系统\n",
    "    ![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 股票信息查询系统\n",
    "    ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 技能目标\n",
    "    - **1. 能实现流程复杂的应用模块**\n",
    "    - **2. 基本技术应用能力。**\n",
    "        - `能使用运算符；`\n",
    "        - `能根据条件控制流程；`\n",
    "        - `能在开发中使用循环控制程序流程；`\n",
    "        - `能在循环中停止程序的循环流程；`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 知识点目标\n",
    "    - **1. 掌握运算符语法、功能与应用；**\n",
    "        - `掌握算术运算符号；`\n",
    "        - `掌握条件运算符号；`\n",
    "        - `掌握逻辑运算符号；`\n",
    "        - `掌握位运算符号；`\n",
    "        - `掌握复合运算符号；`\n",
    "        - `掌握其他运算符号；`\n",
    "        - `掌握表达式与运算符号的优先级；`\n",
    "        - `掌握常见数据的运算；`\n",
    "            - `数值运算；`\n",
    "            - `逻辑值运算；`\n",
    "            - `序列值运算；`\n",
    "            - `字符串值运算；`\n",
    "            - `字典值运算；`\n",
    "            - `集合值运算；`\n",
    "        - `掌握调用符号；`\n",
    "        - `掌握函数调用符号；`\n",
    "    - **2. 掌握程序流程控制的语法与流程控制过程**\n",
    "        - `掌握条件控制的语法-if；`\n",
    "        - `掌握条件控制的应用-if；`\n",
    "        - `掌握循环控制的语法-while；`\n",
    "        - `掌握循环控制的应用-while；`\n",
    "        - `掌握循环控制的语法-for；`\n",
    "        - `掌握循环控制的应用-for；`\n",
    "        - `掌握循环中断控制语法-continue与break；`\n",
    "        - `了解其他流程控制语法；`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 运算符语法、功能与应用\n",
    "\n",
    "&emsp;&emsp;掌握好运算符号，从四个方面掌握：\n",
    "\n",
    "- 运算符号的使用语法\n",
    "- 运算符号的功能（作用）\n",
    "- 运算符号参与运算的数据要求（数据类型，数据个数）\n",
    "- 运算符号的返回数据类型\n",
    "\n",
    "&emsp;&emsp;特别需要注意的是，在某些运算中，还存在数据类型转换。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 算术运算符号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法\n",
    "    - 算术运算都是二目运算（需要两个数据值参与运算）\n",
    "    - 数据1   运算符  数据2\n",
    "    \n",
    "    - 说明：运算符在中间，数据在运算符两边（前面的数据成为：被运算数【比如：被加数】，后面成为运算数【比如：加数】），这种运算的表达方式，也称表达式，俗称**中继表达式**。\n",
    "\n",
    "2. 算术运算的运算符\n",
    "    - \\+ ：加运算  \n",
    "    - \\- ：减运算\n",
    "    - \\*：乘运算\n",
    "    - / ：除运算\n",
    "    - //：整除运算\n",
    "    - %：求余运算\n",
    "    - \\*\\*：幂运算（指数运算，乘方运算）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "10\n",
      "50\n",
      "3.4\n",
      "2\n",
      "1\n",
      "64\n"
     ]
    }
   ],
   "source": [
    "# 熟悉运算符的功能与使用语法\n",
    "print(20 + 30)\n",
    "print(40 - 30)\n",
    "print(10 * 5)\n",
    "print(17 / 5)\n",
    "print(18 // 7)\n",
    "print(25 % 6)\n",
    "print(8 ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 运算数的数据类型\n",
    "\n",
    "\n",
    "- 加减法\n",
    "    - 对整数，小数 ，逻辑值，复数都可以运算。\n",
    "    - 逻辑值True=1，False=0\n",
    "    - 所有运算的结果与数学知识一致。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "118.88\n",
      "31\n",
      "(60+45.8j)\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# + - \n",
    "print(30 + 30)\n",
    "print(30 + 88.88)\n",
    "print(30 + True)\n",
    "print(30 + (30 + 45.8J) )\n",
    "print(False + True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 乘除法\n",
    "\n",
    "    - 对整数，小数 ，逻辑值，复数都可以运算。\n",
    "    \n",
    "    - 逻辑值True=1，False=0\n",
    "    \n",
    "    - 所有运算的结果与数学知识一致。\n",
    "    \n",
    "    - **注意：**除法中除数不能为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900\n",
      "2666.3999999999996\n",
      "30\n",
      "(900+1376.6664j)\n",
      "0\n",
      "---------------\n",
      "1.0\n",
      "0.33753375337533753\n",
      "30.0\n",
      "(0.3002361857994957-0.45836057698723004j)\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# 乘除\n",
    "print(30 * 30)\n",
    "print(30 * 88.88)\n",
    "print(30 * True)\n",
    "print(30 * (30 + 45.88888J) )\n",
    "print(False * True )\n",
    "print(\"---------------\")\n",
    "print(30 / 30)\n",
    "print(30 / 88.88)\n",
    "print(30 / True)\n",
    "print(30 / (30 + 45.8J) )\n",
    "print(False / True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 整除\n",
    "    -  对整数，小数 ，逻辑值都可以运算，对复数不能运算，因为复数不能取整（floor运算）\n",
    "    \n",
    "    - 逻辑值True=1，False=0\n",
    "    \n",
    "    - 所有运算的结果与数学知识一致。\n",
    "    \n",
    "    - **注意：**整除中除数不能为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0.0\n",
      "30\n",
      "0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# 整除\n",
    "print(30 // 30)\n",
    "print(30 // 88.88)\n",
    "print(30 // True)\n",
    "# print((30 + 45J) // 5 )    # TypeError: can't take floor of complex number.\n",
    "print(False // True )\n",
    "\n",
    "print(86.8 // 67.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 乘方\n",
    "    -  对整数，小数 ，逻辑值，复数都可以运算\n",
    "    \n",
    "    - 逻辑值True=1，False=0\n",
    "    \n",
    "    - 所有运算的结果与数学知识一致。\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900\n",
      "4929.503017546495\n",
      "30\n",
      "(-155250+30375j)\n",
      "0\n",
      "2.7017015190288315e+131\n",
      "(-9.592967312139688+17.20460459222708j)\n"
     ]
    }
   ],
   "source": [
    "# 整除\n",
    "print(30 ** 2)\n",
    "print(30 ** 2.5)\n",
    "print(30 ** True)\n",
    "print((30 + 45J) ** 3 )    # TypeError: can't take floor of complex number.\n",
    "print(False ** True )\n",
    "\n",
    "print(86.8 ** 67.8)\n",
    "print(2 ** (4.3 + 3J))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 返回类型\n",
    "&emsp;&emsp;逻辑值作为整数在处理；返回类型的规则\n",
    "\n",
    "\n",
    "- **整数** op **整数**  -> **整数**（op：+ - * //  %  **）\n",
    "- **整数** / **整数** ->  **小数**  (就算能整除，也是小数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(type(16/3))   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **整数** op **小数** -> **小数**\n",
    "- **小数** op **小数** -> **小数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n",
      "<class 'float'>\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(type(4.5//3.5))\n",
    "print(type(4//3.0))\n",
    "print(type(4//3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **整数** op **复数**  -> **复数**\n",
    "- **小数** op **复数**  -> **复数**\n",
    "\n",
    "\n",
    "- **提示**：复数有可能**虚部**为 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92+115j)\n",
      "(1+0j)\n",
      "(41+0j)\n"
     ]
    }
   ],
   "source": [
    "print(23 * (4 + 5J))\n",
    "print( (4 + 5J) / (4 + 5J))\n",
    "\n",
    "print( (4 + 5J) * (4 - 5J) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对序列的运算\n",
    "    - 列表包含三类：元组，列表，字符串\n",
    "    - 有列表支持的算术运算\n",
    "        - **复制：**整数 * 列表：  `整数 * 列表`   或者   `列表 * 整数`\n",
    "        - **连接：**列表 + 列表： `列表 + 列表` \n",
    "        \n",
    "    - 其他的运算符都不支持\n",
    "    \n",
    "    - 注意：只有同类型才能连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 1, 2, 3, 1, 2, 3)\n",
      "(1, 2, 3, 1, 2, 3, 1, 2, 3)\n",
      "[1, 2, 3, 1, 2, 3, 1, 2, 3]\n",
      "[1, 2, 3, 1, 2, 3, 1, 2, 3]\n",
      "hellohellohello\n",
      "worldworldworld\n",
      "[1, 2, 3, 4, 5, 6]\n",
      "(1, 2, 3, 1, 2, 3)\n",
      "helloworld\n"
     ]
    }
   ],
   "source": [
    "print( (1, 2, 3) * 3 )\n",
    "print( 3 * (1, 2, 3))\n",
    "print( [1, 2, 3] * 3 )\n",
    "print( 3 * [1, 2, 3])\n",
    "print('hello'* 3 )\n",
    "print( 3 * 'world')\n",
    "\n",
    "print( [1, 2, 3] + [4, 5, 6])\n",
    "# print( (1, 2, 3) + [4, 5, 6])    # 不同类型：TypeError: can only concatenate tuple (not \"list\") to tuple\n",
    "print( (1, 2, 3) + (1, 2, 3))\n",
    "print( 'hello' + 'world')\n",
    "# print( 3 * {1, 2, 3})     # unsupported operand type(s) for *: 'int' and 'set'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对字典/集合的运算\n",
    "    - 算术运算中的任何运算符，都不支持对字典/集合的运算；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'dict' and 'dict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-128-d6eb1a53c94e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Louis'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'age'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# TypeError: unsupported operand type(s) for +: 'dict' and 'dict'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'dict' and 'dict'"
     ]
    }
   ],
   "source": [
    "dt = {'name': 'Louis', 'age': 20}\n",
    "print(dt + dt)   # TypeError: unsupported operand type(s) for +: 'dict' and 'dict'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 关于@运算\n",
    "    - 在Python中提供了一个 `@` 运算，我们总是把他归为算术运算，实际这个运算对内置类型都没有实现，但这个运算符提供了Python的发展方向与期望的强项：数值计算。因为这个运算是面向大学线性代数中的运算：**内积运算**。\n",
    "    - 该运算方式在NumPy提供了实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "a @ b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关系运算符号；\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法\n",
    "    - 关系运算是二目运算\n",
    "    - 运算数    关系运算符    运算数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 条件运算的运算符\n",
    "    - \\<        ：小于比较运算\n",
    "    - \\<=      ：小于等于比较运算\n",
    "    - \\>        ：大于比较运算\n",
    "    - \\>=      ：大于等于比较运算\n",
    "    - ==      ：相等比较运算\n",
    "    - \\!=       ：不等比较运算\n",
    "3. 返回的类型\n",
    "    - 返回类型都是逻辑型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(45 < 50)\n",
    "print(45 <= 50)\n",
    "print(45 > 50)\n",
    "print(45 >= 50)\n",
    "print(45 == 50)\n",
    "print(45 != 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 运算数的数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 整数，小数，逻辑类型\n",
    "    - 数值类型的比较容易理解\n",
    "    - 数值类型包含True。\n",
    "    \n",
    "    - 注意：不支持复数的比较（这个符合我们高中的数学知识）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(True > False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 所以类型\n",
    "    - 所有类型都支持 ： == 与 !=\n",
    "    - 这两个关系运算符，在不同数据类型的实现的含义不同。\n",
    "        - 对大部分对象而言，指的是两个变量是否是同一个内存空间。\n",
    "        - 对数值类型而言，指的是内容值是否相等。\n",
    "        - 对数值类型而言，首先判断类型是否相同，然后再判定值是否相同。（默认实现：地址相等判定）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print((4 + 5J)==complex(4 , 5))\n",
    "print((4 + 5J)!=(4 + 5J))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print([1,2,3] == [1, 2, 3])\n",
    "print([1,2,3] == (1, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0X111655748 0X111E85AC8\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# 类型同，值同，但变量的内存空间不同（地址不同）\n",
    "a = {1, 2, 3}\n",
    "b = set([1, 2, 3])\n",
    "print('0X%X'%id(a), '0X%X'%id(b) )\n",
    "print(a == b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True]\n"
     ]
    }
   ],
   "source": [
    "# 另外一种对象相同，返回的不是逻辑类型，而是逻辑数组。\n",
    "import numpy as np\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([1,2,3])\n",
    "print(a==b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# 地址相等\n",
    "class A:\n",
    "    pass\n",
    "\n",
    "oa = A()\n",
    "ob = A()\n",
    "print(oa == ob)   # 判定的是地址相等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 序列类型\n",
    "    - 按照字典排序比较，返回除相等外的第一个元素的比较结果。\n",
    "    - 这种比较基本上没有意义，如果想实现比较，需要自己实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print( [6,3,5] <= [6, 2, 5] )\n",
    "print( [6,2,5] <= [6, 2, 5] )\n",
    "print( [6,3,5] <= [6, 2, 5,] )\n",
    "print( [6,2,5] <= [6, 2, 5, 0] )\n",
    "print('hello' <= 'hEllo')    # 每个字符作为ASCII码比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 逻辑运算符号；\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法\n",
    "    - 逻辑运算有二目运算，也有一目运算\n",
    "    - 二目运算：`运算数`    逻辑运算符   `运算数`\n",
    "    - 一目运算：逻辑运算符   `运算数`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 逻辑运算的运算符\n",
    "    - 二目运算：`and    or`\n",
    "    - 一目运算：`not`\n",
    "3. 逻辑运算返回值\n",
    "    - 返回：逻辑类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(True and False)\n",
    "print(True or False)\n",
    "print(not False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 逻辑运算的数据类型 \n",
    "    - 实际上逻辑值是0与1两个整数表示，在Python的逻辑运算中，其他类型会自动转换为逻辑类型后，进行逻辑运算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 整数的逻辑运算\n",
    "    - 所有整数当成逻辑值处理（0：False，非0：True）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "-2\n",
      "2\n",
      "False\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(not -2)\n",
    "print(not -0)\n",
    "print(2 and -2)\n",
    "print(2 or -2)\n",
    "print(2 and False )\n",
    "print(2 or False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 小数的逻辑运算\n",
    "    - 小数与整数一样，也直接作为逻辑值使用（0：False，非0：True）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "-0.0\n",
      "1.8\n",
      "False\n",
      "2.8\n"
     ]
    }
   ],
   "source": [
    "print(not 2.0 )\n",
    "print(not 0.0 )\n",
    "print(2.5 and -0.0)\n",
    "print(1.8 or -2.3)\n",
    "print(2.8 and False )\n",
    "print(2.8 or False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 字符串的逻辑运算\n",
    "    - 字符串也当成逻辑值处理：（''：False，非''：True）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "\n",
      "world\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "print(not 'hello')\n",
    "print(not '')\n",
    "print('' and 'world')         #第一个为False，返回第一个\n",
    "print('Hello' and 'world')         #第一个为True，返回第二个\n",
    "print('hello' or 'world')     \n",
    "# 为什么不直接返回True或者False。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 列表的逻辑运算\n",
    "    - 列表也当成逻辑值处理：（\\[\\]，()：False，非\\[\\]：True）\n",
    "    - 元组(0)也是当作False处理。这个与元组的语法有关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "-0.0\n",
      "1.8\n",
      "[1, 2, 3]\n",
      "--------------\n",
      "True\n",
      "True\n",
      "False\n",
      "-0.0\n",
      "1.8\n",
      "(1, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "print(not [] )\n",
    "print(not [0] )\n",
    "print([1, 2, 3] and -0.0)\n",
    "print(1.8 or [1,2,3])\n",
    "print(2.8 and [1,2,3] )\n",
    "print('--------------')\n",
    "print(not () )\n",
    "print(not (0) )\n",
    "print(not (0,2,3))\n",
    "print((1, 2, 3) and -0.0)\n",
    "print(1.8 or (1,2,3))\n",
    "print(2.8 and (1,2,3) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 字典与集合的逻辑运算\n",
    "    - 字典也当成逻辑值处理：（{}：False，非{}：True）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "-0.0\n",
      "1.8\n",
      "{1, 2, 3}\n"
     ]
    }
   ],
   "source": [
    "print(not {})\n",
    "print(not {0})\n",
    "print({1, 2, 3} and -0.0)\n",
    "print(1.8 or {1,2,3})\n",
    "print(2.8 and {1,2,3} )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对象的逻辑运算\n",
    "    对象作为逻辑值使用：对象不存在None表示False，对象存在表示True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "class  A:\n",
    "    pass\n",
    "\n",
    "a = A()\n",
    "print(not a)\n",
    "# 对象默认为True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 无穷大与非数值，None的逻辑运算\n",
    "    - 无穷大与非数值默认是True（因为非0）\n",
    "    - None当作False使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "nan = float('NaN')\n",
    "inf = float('Inf')\n",
    "n = None\n",
    "print(not n)\n",
    "print(not nan)   \n",
    "print(not inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 在其他的一些实现中，也可能实现逻辑运算发，其运算规则需要看实现文档。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "na = np.array([1,2,3])\n",
    "print(na)\n",
    "print(not na.all())   # 多值，产生歧义：ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n",
    "\n",
    "# ndarray数组对象，不能直接做逻辑值使用，需要做处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 位运算符号\n",
    "- 位运算符分成两类：\n",
    "    - 位移运算符：对数据的位进行移动，从而改变数据的值。\n",
    "    - 位算术运算符：对数据的位进行且，或，异或，否运算。  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 位移运算符号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法：\n",
    "    - 位移运算符是二目运算符\n",
    "    - 语法： **被位移数**    位移运算符    **位移的位数**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 位移运算的运算符\n",
    "    - \\<\\<   ：左移\n",
    "    - \\>\\>   ：右移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "2\n",
      "107150860718626732094842504906000181056140481170553360744375038837035105112493612249319837881569585812759467291755314682518714528569231404359845775746985748039345677748242309854210746050623711418779541821530464749835819412673987675591655439460770629145711964776865421676604298316526243868372056680693760\n"
     ]
    }
   ],
   "source": [
    "print(10<<2)\n",
    "print(10>>2)\n",
    "print(10<<1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 运算类型\n",
    "    - 位移的位数必须是整数，不支持小数\n",
    "    - 逻辑值可以当成整数处理True当成1，False当成0\n",
    "    \n",
    "    - 被位移数也必须是整数 \n",
    "    \n",
    "    - 因为其他数据位移会破坏数据类型与数据结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(10<<True) \n",
    "# print(10<<2.0)   错误\n",
    "# print(2.0<<2)   错误\n",
    "print(True<<2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 返回类型\n",
    "    - 位移返回类型与被位移的整数一样的类型。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 位算术运算符号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法\n",
    "    - 位算术运算符，也分成单目运算与二目运算：\n",
    "    - 二目位算术运算：**整数**    位运算符    **整数**\n",
    "    - 单目位算术运算：**整数**    位运算符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 位算术运算的运算符\n",
    "    - 单目运算：\n",
    "        - ~：每个位取反\n",
    "    - 二目运算：\n",
    "        - &：每个位做且运算（都是1，为1，否则为0）   \n",
    "        - | ：每个位做或运算（一个为1，为1，否则为0）\n",
    "        - ^：每个位做异或运算（同为0，不同为1）\n",
    "\n",
    "\n",
    "3. 返回类型：\n",
    "    - 返回的也是整数（可以当逻辑值使用）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "15\n",
      "15\n",
      "-6\n"
     ]
    }
   ],
   "source": [
    "print(10 & 5)\n",
    "print(10 | 5)\n",
    "print(10 ^ 5)\n",
    "print(~ 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 位运算的经典应用\n",
    "    - IP计算：IP地址是4字节整数，一般表示成数点格式：192.168.1.128\n",
    "    - 颜色计算：颜色就是一个像素，在计算机种使用4字节整数表示（这是模拟全真色，还有其他格式），可以通过位运算取出R，G，B，Alpha四个颜色分量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "把三原色+透明度合成一个像素： 3362002175\n"
     ]
    }
   ],
   "source": [
    "r = 200\n",
    "g = 100\n",
    "b = 20\n",
    "a = 255\n",
    "print(\"把三原色+透明度合成一个像素：\", r<< 24 | g << 16 | b << 8 | a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "红色： 200\n",
      "绿色： 100\n",
      "蓝色： 20\n",
      "透明： 255\n"
     ]
    }
   ],
   "source": [
    "color = 3362002175\n",
    "print('红色：', color >> 24)\n",
    "print('绿色：', color >> 16 & 255)\n",
    "print('蓝色：', color >> 8 & 255)\n",
    "print('透明：', color & 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "表示IP地址的整数 3232235904\n"
     ]
    }
   ],
   "source": [
    "field1 = 192\n",
    "field2 = 168\n",
    "field3 = 1\n",
    "field4 = 128\n",
    "print(\"表示IP地址的整数\", field1<< 24 | field2 << 16 | field3 << 8 | field4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IP地址数点格式：192.168.1.128\n"
     ]
    }
   ],
   "source": [
    "ip = 3232235904\n",
    "field1 = ip >> 24 & 255\n",
    "field2 = ip >> 16 & 255\n",
    "field3 = ip >> 8 & 255\n",
    "field4 = ip >> 0 & 255\n",
    "print(F'IP地址数点格式：{field1}.{field2}.{field3}.{field4}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复合运算符号；\n",
    "- 符合运算指的是：=与前面某些运算符号结合的运算符号。有点人成为增值运算；\n",
    "1. 语法：\n",
    "    - **被运算变量**  符合运算符   **运算数**\n",
    "\n",
    "2. 复合运算的运算符号\n",
    "    - += ：递加\n",
    "    - -= ：递减\n",
    "    - \\*= ：递乘\n",
    "    - /= ：递除\n",
    "    - //= ：递整除\n",
    "    - %= ：递求余\n",
    "    - \\*\\*= ：递幂\n",
    "    - \\@= ：递内积（Python对内置类型没有实现）\n",
    "    \n",
    "    - \\&= ：递位且\n",
    "    - \\|= ：递位或\n",
    "    - \\^= ：递位异或\n",
    "    - \\>\\>= ：递右移\n",
    "    - \\<\\<= ：递左移\n",
    "\n",
    "\n",
    "    - **注意：** 符合运算符号有一个显著的特征：\n",
    "        - 被运算数必须是变量（因为=有赋值的含义，这个只对变量有效）。对数值无效。\n",
    "\n",
    "3. 复合运算符的运算说明：\n",
    "    - a +=2 效果上等价于 a = a + 2，其他依次类推。\n",
    "    - 性能上复合运算比对等的单一运算要好（运算量要少）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 20\n",
    "i += 20\n",
    "# 3 += 20    # 错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 其他运算符号；\n",
    "    - ()：优先级运算，参数传递等。\n",
    "    - []：下标运算\n",
    "    - .：成员调用运算\n",
    "    - in：列表元素判定运算（使用与序列：tuple，list，string, set）\n",
    "    - is：判定两个变量是否同一个内存\n",
    "    - 值1  if   条件  else   值2：条件表达式运算\n",
    "    - + -除了在二目运算作为加减运算，如果作为一目运算表示取正与负"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n",
      "2\n",
      "3.141592653589793\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "not ok\n",
      "20\n",
      "-20\n"
     ]
    }
   ],
   "source": [
    "print(8 * (2 + 3 ))  # 改变优先级\n",
    "print((1,2,3,4)[1])    # 取列表中的第二个元素\n",
    "import math\n",
    "print(math.pi)     # 调用math模块的成员：圆周率\n",
    "print( 20 in [1 ,2, 3, 20, 4])\n",
    "print( 20 in {1 ,2, 3, 20, 4})\n",
    "print( 'name' in {'name':'Louis', 'age':20 })   # 对key判定\n",
    "print( 20 in {'name':'Louis', 'age':20 })      # 不对value判定\n",
    "print( 20 is '20')\n",
    "print( 'ok' if 20>30 else 'not ok' )\n",
    "\n",
    "a = 20\n",
    "print(+a)\n",
    "print(-a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 表达式与运算符号的优先级；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 运算符可以多个连续使用，形成表达式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 3 * 2 / 5**2-10 % 3 == 1 +3 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 表达式因为使用多个运算符，每个运算符存在运算的优先级情况。\n",
    "    - 下面是运算优先级    \n",
    "\n",
    "  \n",
    "\n",
    "运算符说明\t|Python运算符\t|优先级\n",
    "-|-|-\n",
    "'expression,...'\t|字符串转换|24\n",
    "{key:datum,...}\t|字典显示|23\n",
    "\\[expression,...\\]\t|列表显示|22\n",
    "(experession,...)\t|绑定或元组显示|21\n",
    "f(arguments...)\t|函数调用|20\n",
    "索引运算符\t|x\\[index\\]或x\\[index:index2\\[:index3\\]\\]\t|18、19\n",
    "属性访问\t|x.attrbute \t|17\n",
    "乘方\t|\\*\\*\t|16\n",
    "按位取反\t|~\t|15\n",
    "符号运算符\t|+或-\t|14\n",
    "乘、除\t|\\*、/、//、%\t|13\n",
    "加、减\t|+、-\t|12\n",
    "位移\t|\\>\\>、\\<\\<\t|11\n",
    "按位与\t|\\&\t|10\n",
    "按位异或\t|^\t|9\n",
    "按位或\t|\\\\|\t8\n",
    "比较运算符\t|==、!=、>、>=、<、<= \t|7\n",
    "is运算符\t|is、is not\t|6\n",
    "in运算符\t|in、not in\t|5\n",
    "逻辑非\t|not\t|4\n",
    "逻辑与\t|and\t|3\n",
    "逻辑或\t|or\t|2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 定制运算优先级\n",
    "    - 上面的优先级是Python解释器默认优先级。\n",
    "    - 可以使用（）优先级运算符，改变上述运算的优先级。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 + 3) * 2 / (5**(2-10 % 3) ) == (1 + 3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下标运算\n",
    "- 下标运算主要在序列数据类型的运算中使用，用来定位序列中的元素。\n",
    "- 语法：\n",
    "    - 序列数据值\\[开始位置：结束位置：步长\\]；\n",
    "    - 其中的位置从0开始。\n",
    "    \n",
    "    - 下面是具体的使用方式。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 按照位置取列表中的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "b\n",
      "(1, 2, 3, 4, 5, 6, 7, 8)\n",
      "[1, 88, 3, 4, 5, 6, 7, 8]\n",
      "abcdefgh\n",
      "[1, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "v1 = (1,2,3,4,5,6,7,8)\n",
    "v2 = [1,2,3,4,5,6,7,8]\n",
    "v3 = 'abcdefgh'\n",
    "# 取值\n",
    "print (v1[1])\n",
    "print (v2[2])\n",
    "print (v3[1])\n",
    "# 修改值\n",
    "# v1[1]=88   # 元组只取值，不赋值\n",
    "v2[1]=88\n",
    "# v3[1]='X'  # 字符串只取值，不赋值\n",
    "print(v1)\n",
    "print(v2)\n",
    "print(v3)\n",
    "# 释放值\n",
    "# del v1[1]    # 元组值不能修改\n",
    "del v2[1]\n",
    "# del v3[1]     # 字符串值不能修改\n",
    "print(v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 取区间的值\n",
    "    - 区间使用的是\\[ ...  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 4)\n",
      "[2, 3, 4]\n",
      "bcd\n"
     ]
    }
   ],
   "source": [
    "v1 = (1,2,3,4,5,6,7,8)\n",
    "v2 = [1,2,3,4,5,6,7,8]\n",
    "v3 = 'abcdefgh'\n",
    "# 取值\n",
    "print (v1[1:4])    # 不包含位置4\n",
    "print (v2[1:4])\n",
    "print (v3[1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 指定步长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4, 6)\n",
      "[2, 4, 6]\n",
      "bdf\n",
      "[1, 77, 3, 88, 5, 99, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "v1 = (1,2,3,4,5,6,7,8)\n",
    "v2 = [1,2,3,4,5,6,7,8]\n",
    "v3 = 'abcdefgh'\n",
    "# 取值\n",
    "print (v1[1:6:2])    # 不包含位置4\n",
    "print (v2[1:6:2])\n",
    "print (v3[1:6:2])\n",
    "v2[1:6:2]= [77,88,99]   #长度要匹配\n",
    "print(v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 负值的使用\n",
    "    - 其中的开始位置，结束位置，步长都可以取负值，负值表示位置计算从结束位置开始"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "(4, 5, 6, 7)\n",
      "(8, 7, 6, 5)\n"
     ]
    }
   ],
   "source": [
    "v1 = (1,2,3,4,5,6,7,8)\n",
    "v2 = [1,2,3,4,5,6,7,8]\n",
    "v3 = 'abcdefgh'\n",
    "# 取值\n",
    "print(v1[-1])\n",
    "\n",
    "print(v1[-5:-1])    # 从倒数第5个，到倒数第1个\n",
    "print(v1[-1:-5:-1])   # 从倒数第1个，到倒数第5个。反序。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 下标中的默认值\n",
    "    - 下标中每一个值可以使用默认值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5, 6, 7, 8)\n",
      "(1, 2, 3, 4, 5, 6, 7, 8)\n",
      "(3, 4, 5, 6, 7, 8)\n",
      "(1, 2, 3)\n",
      "(1, 3, 5, 7)\n",
      "(8, 7, 6, 5, 4, 3, 2, 1)\n"
     ]
    }
   ],
   "source": [
    "v1 = (1,2,3,4,5,6,7,8)\n",
    "v2 = [1,2,3,4,5,6,7,8]\n",
    "v3 = 'abcdefgh'\n",
    "# 取值\n",
    "#print(v1[])   # 必须要一个值\n",
    "\n",
    "print(v1[:])    # 开始位置，默认是0，结束位置默认是-1的下一个。\n",
    "print(v1[::])   # 步长默认是1。\n",
    "\n",
    "print(v1[2:])    # 开始位置，默认是0，结束位置默认是-1的下一个。\n",
    "print(v1[:3:])   # 步长默认是1。\n",
    "\n",
    "\n",
    "print(v1[::2])   # 步长默认是1。\n",
    "print(v1[::-1])   # 步长默认是1。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 成员调用符号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 运算符`.` 有两个使用场景\n",
    "    - `.` 用于包与模块的路径。\n",
    "    - `.`用于对象的成员调用。\n",
    "    \n",
    "- 语法：\n",
    "    - `包.包`\n",
    "    - `包.模块`\n",
    "    - `模块.类`或者`模块.函数`或者`模块.变量`\n",
    "    - `对象 .成员`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793\n",
      "0.05233595624294383\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(math.pi)\n",
    "print(math.sin(math.pi/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019\n",
      "3\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "from datetime import  *\n",
    "dt = date.today()\n",
    "print(dt.year)\n",
    "print(dt.month)\n",
    "print(dt.day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 函数调用符号；\n",
    "    - 语法\n",
    "        返回值变量 =  函数名(参数，参数，参数 ，......)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49999999515173094\n"
     ]
    }
   ],
   "source": [
    "from math import *\n",
    "print(sin(3.14159262/6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 程序流程控制的语法与流程控制过程\n",
    "- 程序的执行是按照代码的先后执行的（线性性），需要的时候，可以改变程序执行的流程，我们称为流程控制。 \n",
    "- 常规来说，在语言层面可以控制程序的流程提供两种改变方式：\n",
    "    - 执行还是不执行：根据条件选在是否执行，我们称为条件控制\n",
    "    - 反复执行：根据条件反复执行指定的代码。我们称为循环控制\n",
    "        - 循环的结束，我们称为中断控制。\n",
    "- 条件控制与循环控制的语法：\n",
    "    - 使用是块的方式控制,语法：-块头  + 块体\n",
    "    - 块头：每种类型的块头都不一样。\n",
    "    - 块体：由代码构成，所有代码缩行数相同的代码都是同一个块体。直到缩行数不同，表示当前块结束，另外一个块开始。\n",
    "    \n",
    "    - **注意：**默认Python程序有一个顶层块，顶层块是不用缩行的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握条件控制的语法-if；\n",
    "1. 语法一：一个块构成\n",
    "    - 条件控制头 ：` if   逻辑值  ：`\n",
    "    - 说明：条件为真，块得到执行。条件为假，块不会执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "被执行---1\n",
      "被执行---2\n"
     ]
    }
   ],
   "source": [
    "if  True :\n",
    "    print('被执行---1')\n",
    "    print('被执行---2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if  False :    # 不会被执行\n",
    "    print('被执行---1')\n",
    "    print('被执行---2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 语法二：两个块构成\n",
    "\n",
    "```python\n",
    "    if 逻辑值 :\n",
    "        语句\n",
    "         ...\n",
    "    else:\n",
    "        语句\n",
    "        ....\n",
    "```\n",
    "- **说明：**\n",
    "    \n",
    "    - else块不能单独使用，必须与if块一起使用。if块可以不使用else块。\n",
    "    - 当if为假执行else的块，真就执行if的块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "被执行---1\n",
      "被执行---2\n"
     ]
    }
   ],
   "source": [
    "if  True :\n",
    "    print('被执行---1')\n",
    "    print('被执行---2')\n",
    "else:\n",
    "    print('被执行---其他1')\n",
    "    print('被执行---其他2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "被执行---其他1\n",
      "被执行---其他2\n"
     ]
    }
   ],
   "source": [
    "if  False:\n",
    "    print('被执行---1')\n",
    "    print('被执行---2')\n",
    "else:\n",
    "    print('被执行---其他1')\n",
    "    print('被执行---其他2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 语法三：\n",
    "\n",
    "\n",
    "```\n",
    "if 逻辑值:\n",
    "    语句\n",
    "     ...\n",
    "elif 逻辑值:\n",
    "    语句\n",
    "    ...\n",
    "elif 逻辑值:\n",
    "    语句\n",
    "    ...\n",
    "...\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "良\n"
     ]
    }
   ],
   "source": [
    "a = 80\n",
    "if a==100:\n",
    "    print('满分')\n",
    "elif a >=90:\n",
    "    print('优秀')\n",
    "elif a >=80:\n",
    "    print('良')    \n",
    "elif a >=70:\n",
    "    print('中')\n",
    "elif a >=60:\n",
    "    print('及格')\n",
    "elif a<60:\n",
    "    print('不及格')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 语法四：\n",
    "\n",
    "\n",
    "```\n",
    "if 逻辑值:\n",
    "    语句\n",
    "     ...\n",
    "elif 逻辑值:\n",
    "    语句\n",
    "    ...\n",
    "elif 逻辑值:\n",
    "    语句\n",
    "    ...\n",
    "else:\n",
    "    ...\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不及格\n"
     ]
    }
   ],
   "source": [
    "a = 40\n",
    "if a==100:\n",
    "    print('满分')\n",
    "elif a >=90:\n",
    "    print('优秀')\n",
    "elif a >=80:\n",
    "    print('良')    \n",
    "elif a >=70:\n",
    "    print('中')\n",
    "elif a >=60:\n",
    "    print('及格')\n",
    "else:\n",
    "    print('不及格')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握条件控制的应用-if；\n",
    "&emsp;&emsp;除了直接使用逻辑值，还可以使用如下形式的逻辑值，会自动转换。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 使用关系运算\n",
    "    - 因为关系运算直接返回逻辑值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (ok： 接受，其他：不接受) ok\n",
      "你选择了接受！\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (ok： 接受，其他：不接受) ')\n",
    "if a == 'ok':\n",
    "    print('你选择了接受！')\n",
    "else:\n",
    "    print('你选择了拒绝！')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 直接使用数值，对象作为条件\n",
    "    - 这些数值对象可以直接转换为逻辑条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (任何输入： 接受，不输入：不接受) \n",
      "你选择了拒绝！\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (任何输入： 接受，不输入：不接受) ')\n",
    "if a :\n",
    "    print('你选择了接受！')\n",
    "else:\n",
    "    print('你选择了拒绝！')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你选择了接受！\n"
     ]
    }
   ],
   "source": [
    "a =(1,2,3,4)\n",
    "if a :\n",
    "    print('你选择了接受！')\n",
    "else:\n",
    "    print('你选择了拒绝！')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你选择了接受！\n"
     ]
    }
   ],
   "source": [
    "a = 20\n",
    "if a :\n",
    "    print('你选择了接受！')\n",
    "else:\n",
    "    print('你选择了拒绝！')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用in，is等运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (只能选择1，2，3，4) 1\n",
      "输入的是str\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (只能选择1，2，3，4) ')\n",
    "if type(a) is str:\n",
    "    print('输入的是str')\n",
    "else:\n",
    "    print('输入的是非str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (只能选择1，2，3，4) 1\n",
      "选择正确\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (只能选择1，2，3，4) ')\n",
    "if a in ('1' , '2', '3', '4'):\n",
    "    print('选择正确')\n",
    "else:\n",
    "    print('选择不对')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  使用not改变条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (只能选择1，2，3，4) 1\n",
      "1\n",
      "选择正确\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (只能选择1，2，3，4) ')\n",
    "print(a)\n",
    "if a not in ('1' , '2', '3', '4'):\n",
    "    print('选择不对')\n",
    "else:\n",
    "    print('选择正确')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入选择 (只能选择1，2，3，4) 4\n",
      "4\n",
      "选择正确\n"
     ]
    }
   ],
   "source": [
    "a = input('输入选择 (只能选择1，2，3，4) ')\n",
    "print(a)\n",
    "if not a in ('1' , '2', '3', '4'):\n",
    "    print('选择不对')\n",
    "else:\n",
    "    print('选择正确')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握循环控制的语法-while；\n",
    "&emsp;&emsp;while用来对块中代码循环执行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 语法一：\n",
    "\n",
    "```python\n",
    "    while  逻辑值:\n",
    "        语句\n",
    "        ...\n",
    "```\n",
    "\n",
    "- **注意：**\n",
    "    - 逻辑值为True，就执行块，块执行完毕，再重新回到while，继续判定逻辑值是否为True。\n",
    "    - 逻辑值为False，块就不执行。\n",
    "    \n",
    "    - 与if的差别在于：\n",
    "        - if执行完块，就继续执行。\n",
    "        - while执行完块，会回到while处继续判定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "i = 10 \n",
    "while i:\n",
    "    print(i)\n",
    "    \n",
    "    i -= 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 语法二：\n",
    "\n",
    "\n",
    "```python\n",
    "\n",
    "while 逻辑值:\n",
    "    语句\n",
    "    ....\n",
    "else:\n",
    "    语句\n",
    "    ....\n",
    "    \n",
    "```\n",
    "\n",
    "- 当while的条件为False，else会得到执行。\n",
    "- **说明：**\n",
    "    - while支持else，但不支持与elif块配合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "循环没有执行了\n"
     ]
    }
   ],
   "source": [
    "i = 10 \n",
    "while i != 0:\n",
    "    print(i)\n",
    "    \n",
    "    i -= 1\n",
    "else:\n",
    "    print('循环没有执行了')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握循环控制的应用-while；\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. while中的条件使用其他非逻辑值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "循环没有执行了\n"
     ]
    }
   ],
   "source": [
    "i = 10 \n",
    "while i:\n",
    "    print(i)\n",
    "    \n",
    "    i -= 1\n",
    "else:\n",
    "    print('循环没有执行了')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. while中只有一个语句行\n",
    "    - 如果一行，可以在while于else后面直接写语句\n",
    "    - 为了在一行上，编写多个语句，可以使用；分隔。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "循环没有执行了\n"
     ]
    }
   ],
   "source": [
    "i = 10 \n",
    "while i:print(i);i -= 1\n",
    "else:print('循环没有执行了')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-19-db0f5eefeb21>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-19-db0f5eefeb21>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    i -= 1\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "# 只有一行才能在while后面直接写，多行不行。\n",
    "i = 10 \n",
    "while i:print(i)\n",
    "    i -= 1\n",
    "else:print('循环没有执行了')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 避免陷入死循环\n",
    "    - 需要使用变量来控制while后的条件改变，并有机会得到True的值。\n",
    "    - 一般为了避免while死循环，有如下编程套路\n",
    "        - 在while外面定义一个变量，并设置一个初始值\n",
    "        - 在while的逻辑值处，就是用该变量产生。\n",
    "        - 在while内部，根据情况，改变变量，需要退出，就让变量的值改变，以至于使得while逻辑值处的条件得到False的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "while i <=10 :\n",
    "    print(i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握循环控制的语法-for；\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- for是专门对列表与字段等可以迭代的数据设计的专用循环语句。\n",
    "\n",
    "\n",
    "1. 语法一：\n",
    "\n",
    "\n",
    "```python\n",
    "for  标识字  in  序列数据:\n",
    "    语句\n",
    "    ......\n",
    "```\n",
    "\n",
    "- **for执行流程说明：**\n",
    "    - for语句每次会从序列数据开始位置取一个值，并使用标识字定义各一个标量，把取出的值存入变量，然后执行块中语句。\n",
    "    - 当块语句执行完毕，会回到for位置，并取第二个值放入定义的变量，然后重复执行块中语句；\n",
    "    - ....\n",
    "    - 直到序列数据中数据都取玩位置，直接跳出块执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# 循环list\n",
    "for  d in [1,2,3,4]:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "d\n",
      "e\n",
      "f\n",
      "g\n"
     ]
    }
   ],
   "source": [
    "# 循环字符串\n",
    "for  d in 'abcdefg':\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# 循环元组\n",
    "for  d in (1,2,3,4):\n",
    "    print(d)\n",
    "\n",
    "# 元组的另外表示\n",
    "for  d in 1,2,3,4:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# 循环集合\n",
    "for  d in {1,2,3,4}:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name\n",
      "age\n"
     ]
    }
   ],
   "source": [
    "# 循环字典\n",
    "for  d in {'name': 'louis','age':18}:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:louis\n",
      "age:18\n"
     ]
    }
   ],
   "source": [
    "# 循环呢字典的值\n",
    "v = {'name': 'louis','age':18}\n",
    "for  d in v:\n",
    "    print(d, v[d], sep=':')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. for语法二：\n",
    "\n",
    "\n",
    "```python\n",
    "\n",
    "for 变量 in 序列:\n",
    "    语句\n",
    "    ......\n",
    "else:\n",
    "    语句\n",
    "    ......\n",
    "```\n",
    "\n",
    "- **说明：**\n",
    "    - 当列表循环完毕后，else得到执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "循环结束。\n"
     ]
    }
   ],
   "source": [
    "# 循环list\n",
    "for  d in [1,2,3,4]:\n",
    "    print(d)\n",
    "else:\n",
    "    print('循环结束。')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握循环控制的应用-for；\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 使用range创建序列\n",
    "    - Python提供了一个函数，用来创建序列。\n",
    "\n",
    "\n",
    "```python\n",
    " | -  range(stop) -> range object\n",
    " | -  range(start, stop[, step]) -> range object\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# 创建序列范围： [ 0 - 4 )\n",
    "for v in range(4):\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# 创建序列范围： [ start - end )\n",
    "for v in range(1, 5):\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# 创建序列范围： [ start - end ), step = 2\n",
    "for v in range(1, 5, 2):\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 掌握循环中断控制语法-continue与break；\n",
    "&emsp;&emsp;为了结束循环，目前只有等到每次循环结束，重新判定循环条件。Python语言提供了在循环中，可以直接结束循环的语法，使用特殊的语句完成。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### break与continue的理解与使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. 中断循环的两种方式：\n",
    "    - 直接结束循环\n",
    "    - 结束当前次循环后面代码的执行，重新开始循环的条件判别。\n",
    "\n",
    "2. 循环的中断的语法：\n",
    "    - break ：直接结束循环；\n",
    "    - continue ：结束当前循环；\n",
    "    \n",
    "3. **注意：**中断语句只能用于循环：for与while，不能用于其他块中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# break的使用\n",
    "for  v in range(5):\n",
    "    print(v)\n",
    "    break\n",
    "    print('break后')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# continue的使用\n",
    "for  v in range(5):\n",
    "    print(v)\n",
    "    continue\n",
    "    print('break后')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在多重循环中使用break与continue\n",
    "&emsp;&emsp;Python的中断语句与其他语言有些差异，不提供标签，用来中断到指定位置。\n",
    "&emsp;&emsp;对于多重循环来说，中断只影响到最近的循环，对外层循环没有影响，需要单独写代码使用中断继续中断。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "外层: 0\n",
      "\t内层: 0\n",
      "\t内层: 1\n",
      "\t内层: 2\n",
      "外层后\n",
      "外层: 1\n",
      "\t内层: 0\n",
      "\t内层: 1\n",
      "\t内层: 2\n",
      "外层后\n",
      "外层: 2\n",
      "\t内层: 0\n",
      "\t内层: 1\n",
      "\t内层: 2\n",
      "外层后\n"
     ]
    }
   ],
   "source": [
    "# continue的在多层循环中使用\n",
    "for y in range(3):\n",
    "    print('外层:', y)\n",
    "    for x in range(3):\n",
    "        print('\\t内层:', x)\n",
    "        continue\n",
    "        print('\\t内层后')\n",
    "    print('外层后')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "外层: 0\n",
      "\t内层: 0\n",
      "外层后\n",
      "外层: 1\n",
      "\t内层: 0\n",
      "外层后\n",
      "外层: 2\n",
      "\t内层: 0\n",
      "外层后\n"
     ]
    }
   ],
   "source": [
    "for y in range(3):\n",
    "    print('外层:', y)\n",
    "    for x in range(3):\n",
    "        print('\\t内层:', x)\n",
    "        break\n",
    "        print('\\t内层后')\n",
    "    print('外层后')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 了解其他流程控制语法；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 其他影响流程执行的方式还有：\n",
    "    - 1. 结束程序：\n",
    "    ```python\n",
    "    sys.exit(状态码)\n",
    "    ```\n",
    "    - 2. 结束函数模块\n",
    "    ```python\n",
    "    return 值\n",
    "    ```\n",
    "    - 3. 对象自动释放\n",
    "    ```python\n",
    "    with   ...  :\n",
    "        语句\n",
    "        ...\n",
    "    ```\n",
    "    - 4. 异常发生\n",
    "    ```python\n",
    "    try:\n",
    "        语句\n",
    "        ......\n",
    "    catch ...: :\n",
    "        语句\n",
    "        ....\n",
    "    finally:\n",
    "        语句\n",
    "        ......\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 阶段应用实战\n",
    "1. 任务描述\n",
    "    - 使用循环与条件实现集合图形的输出\n",
    "    - 使用循环与条件实现反复输入，直到满足条件才结束输入。\n",
    "    - 显示系统某个目录下的文件列表\n",
    "2. 关联知识点：\n",
    "    - 变量定义\n",
    "    - 输入语句\n",
    "    - 输出语句\n",
    "    - 类型转换\n",
    "    - 数据运算（算术、比较、逻辑计算等）\n",
    "    - 循环控制\n",
    "    - 条件控制\n",
    "    - 循环中断控制\n",
    "    - 数学思维方式"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 综合应用实战\n",
    "1. 实现一个录入模块，提示是否继续录入，根据选择，来决定继续录入，还是退出录入。\n",
    "2. 实现股票历史数据反复查询，并显示多条历史数据。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 思考题目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 判定下面语法是否正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "a++b\n",
    "a++++++++b"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "252px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
